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The issue of failed weaning is a critical concern in the intensive care unit (ICU) setting. This scenario occurs when a patient experiences difficulty maintaining spontaneous breathing and ensuring a patent airway within the first 48 hours after the withdrawal of mechanical ventilation. Approximately 20% of ICU patients experience this phenomenon, which has severe repercussions on their health. It also has a substantial impact on clinical evolution and mortality, which can increase by 25% to 50%. To address this issue, we propose a medical support system that uses a convolutional neural network (CNN) to assess a patient's suitability for disconnection from a mechanical ventilator after a spontaneous breathing test (SBT). During SBT, respiratory flow and electrocardiographic activity were recorded and after processed using time-frequency analysis (TFA) techniques. Two CNN architectures were evaluated in this study: one based on ResNet50, with parameters tuned using a Bayesian optimization algorithm, and another CNN designed from scratch, with its structure also adapted using a Bayesian optimization algorithm. The WEANDB database was used to train and evaluate both models. The results showed remarkable performance, with an average accuracy 98 +/- 1.8% when using CNN from scratch. This model has significant implications for the ICU because it provides a reliable tool to enhance patient care by assisting clinicians in making timely and accurate decisions regarding weaning. This can potentially reduce the adverse outcomes associated with failed weaning events.

JTD Keywords: Bayesian optimization algorithm (boa, Continuous wavelet transform (cwt), Convolutional, Extubation, Failur, Intensive-care-unit, Neural network (cnn) from scratch, Respiratory-distress-syndrome, Time-frequency analysis (tfa), Weaning

Giraldo, Beatriz F

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Cardiorespiratory interaction is related to the heart rate variability (HRV) synchronized with respiration. These metrics help to comprehend the autonomic nervous system (ANS) functionality in cardiovascular mechanisms. In this work, we aim to study the HRV in healthy subjects aged 1824 years during the breathing techniques based on deep breaths followed by apnoeas, developed by Wim Hof (WHM). The attributes of all participates have been treated as a group and therefore, separated by gender. A total of 11 intervals have been distinguished: starting of basal respiration (SRI = 1), controlled deep breaths (CDB = 3), long expiratory apnoea (LEA = 3), short inspiratory apnoea (SIA = 3) and ending with basal respiration again (FRI = 1). To strengthen the HRV knowledge extraction from these scenarios, time and frequency analysis is conducted. In general, breathing and apnoea intervals presented significant statistically differences (p < 0.05), heart rate (HR) mean between SRI and FRI (p < 0.001), RR variability of LEA intervals (p < 0.01), root mean square of RR intervals during CDB (p < 0.05), maximum high frequency (HF) peak amplitude between SRI and FRI (p = 0.016), and low frequency (LF) area for LEA intervals (p < 0.001). When performing the frequency analysis, it has been observed that the sympathetic nervous system (SNS) has a higher contribution in the apnoea intervals. In conclusion, the WHM method implementation seems to involve a decrease in the HR. Specific breathing techniques could help to control the body in different conditions.

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Respiratory patterns present great variability, both in healthy subjects and in patients with different diseases and forms of nasal, oral, superficial or deep breathing. The analysis of this variability depends, among others, on the device used to record the signals that describe these patterns. In this study, we propose multivariable regression models to estimate tidal volume (V-T) considering different breathing patterns. Twenty-three healthy volunteers underwent continuous multisensor recordings considering different modes of breathing. Respiratory flow and volume signals were recorded with a pneumotachograph and thoracic and abdominal respiratory inductive plethysmographic bands. Several respiratory parameters were extracted from the volume signals, such as inspiratory and expiratory areas (Area(ins), Area(exp)), maximum volume relative to the cycle start and end (VTins, VTexp), inspiratory and expiratory time (T-ins, T-exp), cycle duration (T-tot), and normalized parameters of clinical interest. The parameters with the greatest individual predictive power were combined using multivariable models to estimate V-T. Their performance were quantified in terms of determination coefficient (R-2), relative error (E-R) and interquartile range (IQR). Using only three parameters, the results obtained for the thoracic band (V Texp, Ttot, Areaexp) were better than those obtained from the abdominal band (VTexp, T-ins, Area(ins)) with R-2 = 0.94 (IQR: 0.07); ER = 6.99 (IQR: 6.12) vs R-2 = 0.91 (IQR: 0.09), E-R = 8.70 (IQR: 4.62). Overall performance increased to R-2 = 0.97 (IQR: 0.02) and E-R = 4.60 (IQR: 3.68) when parameters from the different bands were combined, further improving when was applied to segments with different inspiration-expiration patterns. In particular, the nose-nose E-R = 1.39 (IQR: 0.73), nose-mouth E-R = 2.11 (IQR: 1.23) and mouth-mouth E-R = 2.29 (IQR: 1.44) patterns showed the best results compared to those obtained for basal, shallow and deep breathing.

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Accurate monitoring of respiratory activity can lead to early identification and treatment of possible respiratory failure. However, spontaneous breathing can vary considerably. To quantify this variability, this study aimed at comparing the breathing pattern characteristics obtained from several recording sensors during different breathing types. Respiratory activity was recorded with a pneumotachograph and two inductive plethysmographic bands, thoracic and abdominal, in 23 healthy volunteers (age 21.5 +/- 1.2 years, 13 females). The subjects were asked to breathe at their natural rate, in successive stages: first freely, then through their nose, nose and mouth, mouth alone, and finally deep and shallow. Both band signals were compared to the pneumotach-derived (gold standard) volume signal. The time series of inspiratory and expiratory duration, total cycle duration and tidal volume were estimated from each of these signals, and also from the sum of the thoracic and abdominal bands. This composite signal showed the highest correlation with the volume signal for almost all subjects, and also had a significantly higher correlation with those obtained from the gold standard volume, compared to either band. In general, breathing parameters increased from basal to nose-mouth breathing, had a minimum in shallow breathing and a maximum in deep breathing. Women exhibited a significantly longer exhalation phase than men during deep breathing, in the combined bands and the gold standard volume. In conclusion, variations in respiratory cycle morphology in different breathing types can be well captured by the simple addition of abdominal and thoracic band signals.

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We aimed to characterize the cerebral hemodynamic response to obstructive sleep apnea/hypopnea events, and evaluate their association to polysomnographic parameters. The characterization of the cerebral hemodynamics in obstructive sleep apnea (OSA) may add complementary information to further the understanding of the severity of the syndrome beyond the conventional polysomnography.Severe OSA patients were studied during night sleep while monitored by polysomnography. Transcranial, bed-side diffuse correlation spectroscopy (DCS) and frequency-domain near-infrared diffuse correlation spectroscopy (NIRS-DOS) were used to follow microvascular cerebral hemodynamics in the frontal lobes of the cerebral cortex. Changes in cerebral blood flow (CBF), total hemoglobin concentration (THC), and cerebral blood oxygen saturation (StO2) were analyzed.We considered 3283 obstructive apnea/hypopnea events from sixteen OSA patients (Age (median, interquartile range) 57 (52-64.5); females 25%; AHI (apnea-hypopnea index) 84.4 (76.1-93.7)). A biphasic response (maximum/minimum followed by a minimum/maximum) was observed for each cerebral hemodynamic variable (CBF, THC, StO2), heart rate and peripheral arterial oxygen saturation (SpO2). Changes of the StO2 followed the dynamics of the SpO2, and were out of phase from the THC and CBF. Longer events were associated with larger CBF changes, faster responses and slower recoveries. Moreover, the extrema of the response to obstructive hypopneas were lower compared to apneas (p < .001).Obstructive apneas/hypopneas cause profound, periodic changes in cerebral hemodynamics, including periods of hyper- and hypo-perfusion and intermittent cerebral hypoxia. The duration of the events is a strong determinant of the cerebral hemodynamic response, which is more pronounced in apnea than hypopnea events.© The Author(s) 2023. Published by Oxford University Press on behalf of Sleep Research Society.

JTD Keywords: cerebral hemodynamics, desaturation, diffuse correlation spectroscopy, duration, hypopnea, hypoxemia, near-infrared spectroscopy, optical pathlength, oxygenation, severity, sleep disorder, spectroscopy, tissue, Adult, Airway obstruction, Apnea hypopnea index, Arterial oxygen saturation, Article, Blood oxygen tension, Blood-flow, Brain blood flow, Brain cortex, Cerebral hemodynamics, Controlled study, Diffuse correlation spectroscopy, Disease severity, Female, Frequency, Frontal lobe, Heart rate, Hemodynamics, Hemoglobin, Hemoglobin determination, Human, Humans, Major clinical study, Male, Near infrared spectroscopy, Near-infrared spectroscopy, Obstructive sleep apnea, Oxygen, Periodicity, Polysomnography, Sleep apnea syndromes, Sleep apnea, obstructive, Sleep disorder, Spectroscopy, near-infrared

A large portion of the elderly population are affected by cardiovascular diseases. Early prognosis of cardiomyopathies remains a challenge. The aim of this study was to classify cardiomyopathy patients by their etiology based on significant indexes extracted from the characterization of the baroreflex mechanism in function of the influence of the cardio-respiratory activity over the blood pressure. Forty-one cardiomyopathy patients (CMP) classified as ischemic (ICM-24 patients) and dilated (DCM-17 patients) were considered. In addition, thirty-nine control (CON) subjects were used as reference. The beat-to-beat (BBI) time series, from the electrocardiographic (ECG) signal, the systolic (SBP), and diastolic (DBP) time series, from the blood pressure signal (BP), and the respiratory time (TT), from the respiratory flow (RF) signal, were extracted. The three-dimensional representation of the cardiorespiratory and vascular activities was characterized geometrically, by fitting a polygon that contains 95% of data, and by statistical descriptive indices. DCM patients presented specific patterns in the respiratory response to decreasing blood pressure activity. ICM patients presented more stable cardiorespiratory activity in comparison with DCM patients. In general, CMP shown limited ability to regulate changes in blood pressure. In addition, patients also shown a limited ability of their cardiac and respiratory systems response to regulate incremental changes of the vascular variability and a lower heart rate variability. The best classifiers were used to build support vector machine models. The optimal model to classify ICM versus DCM patients achieved 92.7% accuracy, 94.1% sensitivity, and 91.7% specificity. When comparing CMP patients and CON subjects, the best model achieved 86.2% accuracy, 82.9% sensitivity, and 89.7% specificity. When comparing ICM patients and CON subjects, the best model achieved 88.9% accuracy, 87.5% sensitivity, and 89.7% specificity. When comparing DCM patients and CON subjects, the best model achieved 87.5% accuracy, 76.5% sensitivity, and 92.3% specificity. In conclusion, this study introduced a new method for the classification of patients by their etiology based on new indices from the analysis of the baroreflex mechanism.Copyright © 2023 Rodriguez, Schulz, Voss, Herrera, Benito and Giraldo.

JTD Keywords: abnormalities, blood pressure variability, cardio-respiratory variability, dilated cardiomyopathy, disease, heart-failure secondary, ischemic cardiomyopathy, ischemic-dilated cardiomyopathy, morphology-relative change, Baroreflex activity, Blood pressure variability, Cardio-respiratory variability, Cheyne-stokes respiration, Ischemic-dilated cardiomyopathy, Morphology-relative change

The optimal extubating moment is still a challenge in clinical practice. Respiratory pattern variability analysis in patients assisted through mechanical ventilation to identify this optimal moment could contribute to this process. This work proposes the analysis of this variability using several time series obtained from the respiratory flow and electrocardiogram signals, applying techniques based on artificial intelligence. 154 patients undergoing the extubating process were classified in three groups: successful group, patients who failed during weaning process, and patients who after extubating failed before 48 hours and need to reintubated. Power Spectral Density and time-frequency domain analysis were applied, computing Discrete Wavelet Transform. A new Q index was proposed to determine the most relevant parameters and the best decomposition level to discriminate between groups. Forward selection and bidirectional techniques were implemented to reduce dimensionality. Linear Discriminant Analysis and Neural Networks methods were implemented to classify these patients. The best results in terms of accuracy were, 84.61 ± 3.1% for successful versus failure groups, 86.90 ± 1.0% for successful versus reintubated groups, and 91.62 ± 4.9% comparing the failure and reintubated groups. Parameters related to Q index and Neural Networks classification presented the best performance in the classification of these patients.

JTD Keywords: Mechanical ventilation, Neural networks, Wavelet transform, Weaning

Prolonged use of mechanical ventilation (MV) can lead to greater complications for a patient. In clinical practice, it is important to identify patients who could fail in the extubation process. However, accurately predicting the outcome of this process remains a challenge. The diaphragm muscle is one of the most active elements in the breathing process. On the other hand, there are several techniques to derive respiratory information from the ECG signal. Signals derived from diaphragmatic activity and from the ECG, such as the envelope of the surface diaphragm electromyographic signal (sEMGi) and the respiratory signal derived from the electrocardiogram (ECG) could contribute to analyze the respiratory response in patients assisted by MV. This work proposes the analysis of the coherence between sEMGi and EDR signals to determine possible differences in the respiratory pattern between successful and failed patients undergoing weaning. 40 patients with MV, candidates for weaning trial process and underwent a spontaneous breathing test were analyzed, classified into: a successful group (SG: 19 patients) that maintained spontaneous breathing after the test, and a failed group (FG: 21 patients) that required reconnection to the MV. The cross correlation, power spectral density and magnitude squared coherence (MSC) of the sEMGi and the EDR signals were estimated. According to the results, the MSC parameters such as area under the curve and mean coherence value presented statistically significance differences between the two groups of patients (p = 0.024). Our results suggest that both sEMGi and EDR signals could provide information about the behavior of the respiratory system in these patients. Clinical Relevance- This study analyzes the correlation and the coherence between the envelope of the surface electromyographic signal and the respiratory signal derived from the ECG to characterize the respiratory pattern of successful and failed patients on weaning process.

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A large portion of the elderly population are affected by cardiovascular diseases. The early prognosis of cardiomyopathies is still a challenge. The aim of this study was to classify cardiomyopathy patients by their etiology in function of significant indexes extracted from the characterization of the recurrence plot of the systems involved. Thirty-nine cardiomyopathy patients (CMP) classified as ischemic (ICM - 24 patients) and dilated (DCM-15 patients) were considered. In addition, thirty-nine control subjects (CON) were used as reference. The beat-to-beat (BBI) time series, from the electrocardiographic signal, the systolic (SBP), and diastolic (DBP) time series, from the blood pressure signal, and the respiratory time (FLW) from the respiratory flow signal, were extracted. The recurrence plot from each signal considered were calculated and characterized by a total of 12 indexes. The best classifiers were used to build support vector machine models. The optimal model to classify ICM versus DCM patients achieved 92.3% accuracy, 95.8% sensitivity, and 86.6% specificity. When comparing CMP patients and CON subjects, the best model achieved 85.8% accuracy, 92.3% sensitivity, and 80.1% specificity. Our results suggest a more deterministic behavior in DCM patients. Clinical Relevance - This study explores the recurrence plot for the classification of ICM and DCM patients.

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Cardiorespiratory Phase Synchronization (CRPS) is the manifestation of the non-linear coupling between the cardiac and the respiratory systems, different to the Respiratory Sinus Arrythmia (RSA). This takes place when the heartbeats occur at the same relative phase of the breathing, during a succession of respiratory cycles. In this study, we investigated the CRPS in 45 elderly patients admitted to the semi-critical unit of a hospital. The patients were classified according to their respiratory state as non-Periodic Breathing (nPB), Periodic Breathing (PB) and Cheyne-Stokes Respiration (CSR). The phase synchrogram between the electrocardiographic and respiratory signals was computed using the Hilbert transform technique. A continuous measure of the CRPS was obtained from the synchrogram, and was characterized by the average duration of synchronized epochs (A vgDurSync), the percentage of synchronized time (%Sync), the number of synchronized epochs (NumSync), and the frequency ratio between the cardiac and respiratory oscillators (FreqRat). These measures were studied using two different thresholds (0.1 and 0.05) for the amplitude of the synchronization and a minimum duration threshold of 10s. According to the results, the AvgDurSync and %Sync had a decreasing trend in patients with breathing periodicity. In addition, CSR patients presented the lowest values A vgDurSync and %Sync. Therefore, the CRPS method could be a useful tool for characterizing periodic respiratory patterns in elderly patients, which might be related to chronic heart failure. Clinical Relevance- This study analyzes the synchronization between cardiac and respiratory systems in elderly patients with a possible progressive decompensation in the cardiac function.

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Weaning from mechanical ventilation in the intensive care unit is a complex and relevant clinical problem. Prolonged mechanical ventilation leads to a variety of medical complications that increase hospital stay and costs, in addition to contributing the morbidity and mortality, affecting long-term quality of life. This work presents a methodology to establish the optimal moment of extubation of a patient connected to a mechanical ventilator, submitted to the T-Tube test. 133 patients are analyzed, classified into two groups: successful group (94 patients) and failed group (39 patients). The behaviour of the respiratory function is characterized through the mean, standard deviation, kurtosis, skewness, interquartile range and coefficient of interval of the respiratory flow time series. To classify these patients, neural networks (NN) and support vector machines (SVM) classifier are used, considering time intervals of the 450s, 600s and 900s. According to the results, the best classification is obtained using the SVM. Clinical Relevance-The paper determines the optimal moment for weaning a patient connected to a mechanical ventilator using machine learning techniques.

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Cardiomyopathies diseases affects a great number of the elderly population. An adequate identification of the etiology of a cardiomyopathy patient is still a challenge. The aim of this study was to classify patients by their etiology in function of indexes extracted from the characterization of the pulse transit time (PTT). This time series represents the time taken by the pulse pressure to propagate through the length of the arterial tree and corresponding to the time between R peak of ECG and the mid-point of the diastolic to systolic slope in the blood pressure signal. For each patient, the PTT time series was extracted. Thirty cardiomyopathy patients (CMP) classified as ischemic (ICM - 15 patients) and dilated (DCM - 15 patients) were analyzed. Forty-three healthy subjects (CON) were used as a reference. The PTT time series was characterized through statistical descriptive indices and the joint symbolic dynamics method. The best indices were used to build support vector machine models. The optimal model to classify ICM versus DCM patients achieved 89.6% accuracy, 78.5% sensitivity, and 100% specificity. When comparing CMP patients and CON subjects, the best model achieved 91.3% accuracy, 91.3% sensitivity, and 88.3% specificity. Our results suggests a significantly lower pulse transit time in ischemic patients.Clinical relevance - This study analyzed the suitability of the pulse transit time for the classification of ICM and DCM patients. © 2021 IEEE.

JTD Keywords: Aged, Blood pressure, Cardiomyopathies, Cardiomyopathy, Cardiomyopathy, dilated, Congestive cardiomyopathy, Human, Humans, Pulse wave, Pulse wave analysis, Support vector machine

In clinical practice, when a patient is undergoing mechanical ventilation, it is important to identify the optimal moment for extubation, minimizing the risk of failure. However, this prediction remains a challenge in the clinical process. In this work, we propose a new protocol to study the extubation process, including the electromyographic diaphragm signal (diaEMG) recorded through 5-channels with surface electrodes around the diaphragm muscle. First channel corresponds to the electrode on the right. A total of 40 patients in process of withdrawal of mechanical ventilation, undergoing spontaneous breathing tests (SBT), were studied. According to the outcome of the SBT, the patients were classified into two groups: successful (SG: 19 patients) and failure (FG: 21 patients) groups. Parameters extracted from the envelope of each channel of diaEMG in time and frequency domain were studied. After analyzing all channels, the second presented maximum differences when comparing the two groups of patients, with parameters related to root mean square (p = 0.005), moving average (p = 0.001), and upward slope (p = 0.017). The third channel also presented maximum differences in parameters as the time between maximum peak (p = 0.004), and the skewness (p = 0.027). These results suggest that diaphragm EMG signal could contribute to increase the knowledge of the behaviour of respiratory system in these patients and improve the extubation process.Clinical Relevance - This establishes the characterization of success and failure patients in the extubation process. © 2021 IEEE.

JTD Keywords: classification, recognition, Airway extubation, Artificial ventilation, Clinical practices, Clinical process, Diaphragm, Diaphragm muscle, Diaphragms, Electrodes, Electromyographic, Extubation, Frequency domain analysis, Human, Humans, Maximum differences, Mechanical ventilation, New protocol, Respiration, artificial, Respiratory system, Risk of failure, Spontaneous breathing, Surface electrode, Surface emg signals, Thorax, Ventilation, Ventilator weaning

Heart diseases are the leading cause of death in developed countries. Ascertaining the etiology of cardiomyopathies is still a challenge. The objective of this study was to classify cardiomyopathy patients through cardio, respiratory and vascular variability analysis, considering the vascular activity as the input and output of the baroreflex response. Forty-one cardiomyopathy patients (CMP) classified as ischemic (ICM, 24 patients) and dilated (DCM, 17 patients) were analyzed. Thirty-nine elderly control subjects (CON) were used as reference. From the electrocardiographic, respiratory flow, and blood pressure signals, following temporal series were extracted: beat-to-beat intervals (BBI), total respiratory cycle time series (TT), and end– systolic (SBP) and diastolic (DBP) blood pressure amplitudes, respectively. Three-dimensional representation of the cardiorespiratory and vascular activities was characterized geometrically, by fitting a polygon that contains 95% of data, and by statistical descriptive indices. The best classifiers were used to build support vector machine models. The optimal model to classify ICM versus DCM patients achieved 92.7% accuracy, 94.1% sensitivity, and 91.7% specificity. When comparing CMP patients and CON subjects, the best model achieved 86.2% accuracy, 82.9% sensitivity, and 89.7% specificity. These results suggest a limited ability of cardiac and respiratory systems response to regulate the vascular variability in these patients.

JTD Keywords: Time series analysis, Support vector machines, Blood pressure, Sensitivity, Indexes, Electrocardiography, Kernel

Respiration rate can be assessed by analyzing respiratory changes of the electrocardiogram (ECG). Several methods can be applied to derive the respiratory signal from the ECG (EDR signal). In this study, four EDR estimation methods based on QRS features were analyzed. A database with 44 healthy subjects (16 females) in supine and sitting positions was analyzed. Respiratory flow and ECG recordings on leads I, II, III and a Chest lead was studied. A QR slope-based method, an RS slope-based method, an QRS angle-based method and an QRS area-based method were applied. Their performance was evaluated by the correlation coefficient with the reference respiratory volume signal. Significantly higher correlation coefficients in the range r = 0.77 – 0.86 were obtained with the Chest lead for all methods. The EDR estimation method based on the QRS angle provided the highest similarity with the volume signal for all recording leads and subject positions. We found no statistically significant differences according to gender or subject position.Clinical Relevance— This work analyzes the EDR signal from four electrocardiographic leads to obtain the respiratory signal and contributes to a simplified analysis of respiratory activity.

JTD Keywords: Electrocardiography, Lead, Estimation, Correlation coefficient, Databases, Heart, Correlation

Cardiovascular diseases are one of the most common causes of death; however, the early detection of patients at high risk of sudden cardiac death (SCD) remains an issue. The aim of this study was to analyze the cardio-vascular couplings based on heart rate variability (HRV) and blood pressure variability (BPV) analyses in order to introduce new indices for noninvasive risk stratification in idiopathic dilated cardiomyopathy patients (IDC). High-resolution electrocardiogram (ECG) and continuous noninvasive blood pressure (BP) signals were recorded in 91 IDC patients and 49 healthy subjects (CON). The patients were stratified by their SCD risk as high risk (IDCHR) when after two years the subject either died or suffered life-threatening complications, and as low risk (IDCLR) when the subject remained stable during this period. Values were extracted from ECG and BP signals, the beat-to-beat interval, and systolic and diastolic blood pressure, and analyzed using the segmented Poincaré plot analysis (SPPA), the high-resolution joint symbolic dynamics (HRJSD) and the normalized short time partial directed coherence methods. Support vector machine (SVM) models were built to classify these patients according to SCD risk. IDCHR patients presented lowered HRV and increased BPV compared to both IDCLR patients and the control subjects, suggesting a decrease in their vagal activity and a compensation of sympathetic activity. Both, the cardio -systolic and -diastolic coupling strength was stronger in high-risk patients when comparing with low-risk patients. The cardio-systolic coupling analysis revealed that the systolic influence on heart rate gets weaker as the risk increases. The SVM IDCLR vs. IDCHR model achieved 98.9% accuracy with an area under the curve (AUC) of 0.96. The IDC and the CON groups obtained 93.6% and 0.94 accuracy and AUC, respectively. To simulate a circumstance in which the original status of the subject is unknown, a cascade model was built fusing the aforementioned models, and achieved 94.4% accuracy. In conclusion, this study introduced a novel method for SCD risk stratification for IDC patients based on new indices from coupling analysis and non-linear HRV and BPV. We have uncovered some of the complex interactions within the autonomic regulation in this type of patient.

JTD Keywords: Idiopathic dilated cardiomyopathy, Heart rate variability, Blood pressure variability, Coupling analysis, Sudden cardiac death, Risk stratification

Cardiovascular diseases are one of the most common causes of death in elderly patients. The etiology of cardiomyopathies is difficult to discern clinically. The objective of this study was to classify cardiomyopathy patients using coupling analysis, through their cardiovascular behavior and the baroreflex response. A total of thirty-eight cardiomyopathy patients (CMP) classified as ischemic (ICM, 25 patients) and dilated (DCM, 13 patients) were analyzed. Thirty elderly control subjects (CON) were used as reference. Their electrocardiographic (ECG) and blood pressure (BP) signals were studied. To characterize the cardiovascular activity, the following temporal series were extracted: beat-to-beat intervals (from the ECG signal), and end- systolic and diastolic blood pressure amplitudes (from the BP signal). Non-linear characterization techniques like high resolution joint symbolic dynamics, segmented Poincaré plot analysis, normalized shorttime partial directed coherence, and dual sequence method were used to characterize these times series. The best indices were used to build support vector machine models for classification. The optimal model for ICM versus DCM patients achieved 84.2% accuracy, 76.9% sensitivity, and 88% specificity. When CMP patients and CON subjects were compared, the best model achieved 95.5% accuracy, 97.3% sensitivity, and 93.3% specificity. These results suggest a disfunction in the baroreflex mechanism in cardiomyopathies patients.

JTD Keywords: Couplings, Time series analysis, Support vector machines, Electrocardiography, Baroreflex, Coherence, Sensitivity

Studies based on the cardiac and respiratory system have allowed a better knowledge of their behavior to contribute with the diagnosis and treatment of diseases associated with them. The main goal of this project was to analyze the behavior of the cardiorespiratory system in healthy subjects, depending on the body position. The electrocardiography and respiratory flow signals were recorded in two positions, supine and sitting. Each signal was analyzed considering sliding windows of 30 s, with and overlapping of 50%. Temporal and spectral features were extracted from each signal. A total of 187 features were extracted for each window. According to statistical analysis, 148 features showed significant differences when comparing the position of the subject. Afterwards, the classifications methods based on decision trees, k-nearest neighbor and support vector machines were applied to identify the best classification model. The most advantageous performance model was obtained with a linear support vector machine method, with an accuracy of 99.5%, a sensitivity of 99.2% and a specificity of 99.6%. In conclusion, we have observed that the position of the body (supine or sitting) could modulate the cardiac and respiratory system response. New statistical models might provide new tools to analyze the behavior of these systems and the cardiorespiratory interaction complexity.

JTD Keywords: Cardiac dynamics, Respiratory dynamics, Statistical models, Supine and sitting posture

Obstructive Sleep Apnea severity is commonly determined after a sleep polysomnographic study by the Apnea-Hypopnea Index (AHI). This index does not contain information about the duration of events, and weights apneas and hypopneas alike. Significant differences in disease severity have been reported in patients with the same AHI. The aim of this work was to study the effect of obstructive event type and duration on the subsequent oxygen desaturation (SaO2) by mixed-effects models. These models allow continuous and categorical independent variables and can model within-subject variability through random effects. The desaturation depth dSaO2, desaturation duration dtSaO2 and desaturation area dSaO2A were analyzed in the 2022 apneas and hypopneas of eight severe patients. A mixed-effects model was defined to account for the influence of event duration (AD), event type, and their interaction on SaO2 parameters. A two-step backward model reduction process was applied for random and fixed effects optimization. The optimum model obtained for dtSaO2 suggests an almost subject-independent proportion increase with AD, which did not significantly change in apneas as compared to hypopneas. The optimum model for dSaO2 reveals a significantly higher increase as a function of AD in apneas than hypopneas. Dependence of on event type and duration was different in every subject, and a subject-specific model could be obtained. The optimum model for SaO2A combines the effects of the other two. In conclusion, the proposed mixed-effects models for SaO2 parameters allow to study the effect of respiratory event duration and type, and to include repeated events within each subject. This simple model can be easily extended to include the contribution of other important factors such as patient severity, sleep stage, sleeping position, or the presence of arousals.

JTD Keywords: Biological system modeling, Sleep apnea, Mathematical model, Indexes, Reduced order systems, Optimization

The time delay estimation problem associated with an ensemble of misaligned, repetitive signals is revisited. Each observed signal is assumed to be composed of an unknown, deterministic signal corrupted by Gaussian, white noise. This paper shows that maximum likelihood (ML) time delay estimation can be viewed as the maximization of an eigenvalue ratio, where the eigenvalues are obtained from the ensemble correlation matrix. A suboptimal, one-step time delay estimate is proposed for initialization of the ML estimator, based on one of the eigenvectors of the inter-signal correlation matrix. With this approach, the ML estimates can be determined without the need for an intermediate estimate of the underlying, unknown signal. Based on respiratory flow signals, simulations show that the variance of the time delay estimation error for the eigenvalue-based method is almost the same as that of the ML estimator. Initializing the maximization with the one-step estimates, rather than using the ML estimator alone, the computation time is reduced by a factor of 5M when using brute force maximization (M denoting the number of signals in the ensemble), and a factor of about 1.5 when using particle swarm maximization. It is concluded that eigenanalysis of the ensemble correlation matrix not only provides valuable insight on how signal energy, jitter, and noise influence the estimation process, but it also leads to a one-step estimator which can make the way for a substantial reduction in computation time.

JTD Keywords: Biomedical signals, Time delay estimation, Eigenanalysis, Ensemble analysis

Several neurological and mechanical non-linear mechanisms relate the respiratory and cardiovascular systems to one another. Besides the well-known modulation of heart rate by respiration, another form of non-linear interaction between both systems is Cardiorespiratory Phase Synchronization (CRPS). In this study we investigated CRPS on a group of 27 healthy individuals subject to a stimulation protocol with five different mental states: a basal state, a videogame, a comedy video, a suspense video and a reading state. Acontinuous measure of CRPS was calculated from the phase synchrogram between respiratory and electrocardiographic signals. Periods of CRPS were characterized by their average duration (AvDurSync) and by the percentage of synchronized time (%Sync) within each mental state. These measures were studied considering two thresholds: a minimum amplitude and a minimum duration for synchronization. Each subject exhibited a particular pattern of phase locking ratios along the different mental states. We observed that, in all states, %Sync decreased and AvDurSync increased in proportion to the minimum duration threshold. Both measures were inversely proportional to the minimum amplitude threshold.uring the videogame, subjects showed a significantly higher %Sync as compared to any other mental stimulus, irrespective of the minimum duration threshold. Mental stimulation can be an alternative approach to enhance cardiorespiratory coupling when subjects have difficulties to perform aerobic exercise, such as in patients with Chronic Obstructive Pulmonary Disease or Chronic Heart failure.

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Physiological records are one of the most relevant elements to obtain objective information from the patients. The presence of artifacts in biomedical signals can give misleading in the analysis of information that these signals give. The blood pressure signal is one of the records clearly affected by different artifacts, especially the ones due from the calibration episodes. We propose a method to reconstruct different episodes of artifacts in these signals. This method is sustained on the detection of the events of the signal, differentiating between to the physiological cycles and the artifacts. The performance of the method is based on the detection of the cycles and artifact's position, the identification of the number of cycles to reconstruct, and the prediction of the cycle model used to generate the missing cycles. The parameter θ E represents the difference between the area under the curve when two events are compared. The value of this parameter is low when two similar events are compared like the physiological cycles, whereas it is high comparing a cycle with an artifact. An adaptive threshold is defined to identify the artifact episodes. The number of cycles to reconstruct is generated considering the same number of their neighbours physiological cycles, to left and right, of the original signal. Finally, the performance of the method has been analyzed comparing the number of events and artifacts detected and their correct reconstruction. According to the results, the reconstruction error was less than 1% in all cases.

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In congestive heart failure (CHF), dilated cardiomyopathy (DCM) and ischemic cardiomyopathy (ICM) are two highly related pathologies that are not fully characterized. The aim of this study is to assess respiratory sinus arrhythmia (RSA) index of the parasympathetic system, in order to discriminate between both pathologies, DCM and ICM. For this, ECG-signals of 49 subjects (12 DCM patients, 21 ICM patients, 6 ICM patients with diabetes mellitus (DM) type II and 10 control subjects) from the database HERIS II and of 173 subjects (50 DCM, 50 ICM, 15 DCM with DM type II, 15 ICM with DM type II and 47 control subjects) from the database MUSIC2 were analyzed. The RSA was quantified using linear and non-linear analysis methods (fractal dimension and entropy). The results showed a significant difference between ICM and DCM subjects (p=0.013) with a sensitivity of 83% and specificity of 90%. Decreasing RSA values were present in CHF patients, especially in ICM patients, in comparison with healthy subjects. Alterations in the parasympathetic system due to DM were also identified.

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Breathing pattern as periodic breathing (PB) in chronic heart failure (CHF) is associated with poor prognosis and high mortality risk. This work investigates the significance of a number of time domain parameters for characterizing respiratory flow cycle morphology in patients with CHF. Thus, our primary goal is to detect PB pattern and identify patients at higher risk. In addition, differences in respiratory flow cycle morphology between CHF patients (with and without PB) and healthy subjects are studied. Differences between these parameters are assessed by investigating the following three classification issues: CHF patients with PB versus with non-periodic breathing (nPB), CHF patients (both PB and nPB) versus healthy subjects, and nPB patients versus healthy subjects. Twenty-six CHF patients (8/18 with PB/nPB) and 35 healthy subjects are studied. The results show that the maximal expiratory flow interval is shorter and with lower dispersion in CHF patients than in healthy subjects. The flow slopes are much steeper in CHF patients, especially for PB. Both inspiration and expiration durations are reduced in CHF patients, mostly for PB. Using the classification and regression tree technique, the most discriminant parameters are selected. For signals shorter than 1 min, the time domain parameters produce better results than the spectral parameters, with accuracies for each classification of 82/78, 89/85, and 91/89 %, respectively. It is concluded that morphologic analysis in the time domain is useful, especially when short signals are analyzed.

JTD Keywords: Chronic heart failure, Ensemble average, Periodic and non-periodic breathing, Respiratory pattern

Patients with Chronic Heart Failure (CHF) often develop oscillatory breathing patterns. This work proposes the characterization of respiratory pattern by Wavelet Transform (WT) technique to identify Periodic Breathing pattern (PB) and Non-Periodic Breathing pattern (nPB) through the respiratory flow signal. A total of 62 subjects were analyzed: 27 CHF patients and 35 healthy subjects. Respiratory time series were extracted, and statistical methods were applied to obtain the most relevant information to classify patients. Support Vector Machine (SVM) were applied using forward selection technique to discriminate patients, considering four kernel functions. Differences between these parameters are assessed by investigating the following four classification issues: healthy subjects versus CHF patients, PB versus nPB patients, PB patients versus healthy subjects, and nPB patients versus healthy subjects. The results are presented in terms of average accuracy for each kernel function, and comparison groups.

JTD Keywords: Chronic heart failure, Forward selection, Non-periodic breathing, Periodic breathing, Support vector machine

Cardiac death risk is still a big problem by an important part of the population, especially in elderly patients. In this study, we propose to characterize and analyze the cardiovascular and cardiorespiratory systems using the Poincaré plot. A total of 46 cardiomyopathy patients and 36 healthy subjets were analyzed. Left ventricular ejection fraction (LVEF) was used to stratify patients with low risk (LR: LVEF > 35%, 16 patients), and high risk (HR: LVEF ≤ 35%, 30 patients) of heart attack. RR, SBP and TTot time series were extracted from the ECG, blood pressure and respiratory flow signals, respectively. Parameters that describe the scatterplott of Poincaré method, related to short- and long-term variabilities, acceleration and deceleration of the dynamic system, and the complex correlation index were extracted. The linear discriminant analysis (LDA) and the support vector machines (SVM) classification methods were used to analyze the results of the extracted parameters. The results showed that cardiac parameters were the best to discriminate between HR and LR groups, especially the complex correlation index (p = 0.009). Analising the interaction, the best result was obtained with the relation between the difference of the standard deviation of the cardiac and respiratory system (p = 0.003). When comparing HR vs LR groups, the best classification was obtained applying SVM method, using an ANOVA kernel, with an accuracy of 98.12%. An accuracy of 97.01% was obtained by comparing patients versus healthy, with a SVM classifier and Laplacian kernel. The morphology of Poincaré plot introduces parameters that allow the characterization of the cardiorespiratory system dynamics.

JTD Keywords: Time series analysis, Electrocardiography, Support vector machines, Kernel, Standards, Correlation, RF signals

Synchronization is a central key feature of neural information processing and communication between different brain areas. Disturbance of oscillatory brain rhythms and decreased synchronization have been associated with different disorders including schizophrenia. The aim of this study was to investigate whether synchronization (in relaxed conditions with no stimuli) between different brain areas within the delta, theta, alpha (alpha1, alpha2), beta (beta1, beta2), and gamma bands is altered in patients with a neurological disorder in order to generate significant cortical enhancements. To achieve this, we investigated schizophrenic patients (SZO; N=17, 37.5±10.4 years, 15 males) and compared them to healthy subjects (CON; N=21, 36.7±13.4 years, 15 males) applying the phase locking value (PLV). We found significant differences between SZO and CON in different brain areas of the theta, alpha1, beta2 and gamma bands. These areas are related to the central and parietal lobes for the theta band, the parietal lobe for the alpha1, the parietal and frontal for the beta2 and the frontal-central for the gamma band. The gamma band revealed the most significant differences between CON and SZO. PLV were 61.7% higher on average in SZO in most of the clusters when compared to CON. The related brain areas are directly related to cognition skills which are proved to be impaired in SZO. The results of this study suggest that synchronization in SZO is also altered when the patients were not asked to perform a task that requires their cognitive skills (i.e., no stimuli are applied - in contrast to other findings).

JTD Keywords: Synchronization, Electroencephalography, Electrodes, Brain, Time series analysis, Oscillators, Frequency synchronization

In this work, the cardiorespiratory pattern of patients undergoing extubation process is studied. First, the respiratory and cardiac signals were resampled, next the Symbolic Dynamics (SD) technique was implemented, followed of a dimensionality reduction applying Forward Selection (FS) and Moving Window with Variance Analysis (MWVA) methods. Finally, the Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM) classifiers were used. The study analyzed 153 patients undergoing weaning process, classified into 3 groups: Successful Group (SG: 94 patients), Failed Group (FG: 39 patients), and patients who had been successful during the extubation and had to be reintubated before 48 hours, Reintubated Group (RG: 21 patients). According to the results, the best classification present an accuracy higher than 88.98 ± 0.013% in all proposed combinations.

JTD Keywords: Support vector machines, Standards, Time series analysis, Resonant frequency, Nonlinear dynamical systems, Ventilation

Obstructive Sleep Apnea (OSA) is a sleep disorder with a high prevalence in the general population. It is a risk factor for many cardiovascular diseases, and an independent risk factor for cerebrovascular diseases such as stroke. After an apnea episode, both arterial blood pressure and cerebral blood flow velocity change in function of the apnea duration (AD). We hypothesized that the relative excursion in heart rate (AHR), defined as the percentage difference between the maximum and the minimum heart rate values associated to an obstructive apnea event, is also related to AD. In this work we studied the relationship between apnea-related AHR and AD in a population of eight patients with severe OSA. AHR and AD showed a moderate but statistically significant correlation (p <; 0.0001) in a total of 1454 obstructive apneas analyzed. The average heart rate excursion for apneas with AD ≥ 30s (ΔHR = 31.29 ± 6.64%) was significantly greater (p = 0.0002) than for apneas with AD ∈ [10,20)s (ΔHR = 18.14±3.08%). We also observed that patients with similar Apnea-Hypopnea Index (AHI) may exhibit remarkably different distributions of AHR and AD, and that patients with a high AHI need not have a higher average AHR than others with a lower severity index. We conclude that the overall apnea-induced heart rate excursion is partially explained by the duration of apnoeic episodes, and it may be a simple measure of the cardiovascular stress associated with OSA that is not directly reflected in the AHI.

JTD Keywords: Heart rate, Sleep apnea, Correlation, Indexes, Sociology, Blood vessels

La enfermedad del Párkinson es un tipo de trastorno del movimiento, causado por la degeneración de las células dopaminérgicas. La variabilidad del ritmo cardíaco en estos pacientes se puede ver alterada como consecuencia de la actividad motora. El estudio de esta variabilidad puede ayudar en el diagnóstico y análisis de la evolución de la enfermedad en estos pacientes. En este estudio se propone el análisis de parámetros extraídos de la señal electrocardiográfica en pacientes enfermos de Párkinson, con el propósito de obtener índices que puedan ser indicadores de esta enfermedad. Se propone un protocolo para registrar la señal ECG considerando 4 actividades diferentes. Se registraron 19 pacientes y 16 sujetos sanos. Las señales fueron analizadas en el dominio temporal considerando los intervalos RR de la señal ECG, y en el dominio frecuencial, considerando las bandas de muy baja (VLF: 0-0.05 Hz), baja (LF: 0.05-0.15 Hz) y alta (HF: 0.15-0.4 Hz) frecuencia, respectivamente. De acuerdo con los resultados obtenidos, el índice de la actividad simpática presentó diferencias estadísticamente significativas al comparar pacientes versus sanos, durante las 4 actividades desarrolladas. El intervalo RR también es un indicador de la variación de la actividad cardíaca en los pacientes, especialmente al compararlos en el estado basal. Índices que relacionen parámetros temporales y frecuenciales podrían ser un claro indicador de la actividad cardiovascular de los pacientes enfermos de Párkinson.

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Changes in the left ventricle function produce alternans in the hemodynamic and electric behavior of the cardiovascular system. A total of 49 cardiomyopathy patients have been studied based on the blood pressure signal (BP), and were classified according to the left ventricular ejection fraction (LVEF) in low risk (LR: LVEF>35%, 17 patients) and high risk (HR: LVEF≤35, 32 patients) groups. We propose to characterize these patients using a linear and a nonlinear methods, based on the spectral estimation and the recurrence plot, respectively. From BP signal, we extracted each systolic time interval (STI), upward systolic slope (BPsl), and the difference between systolic and diastolic BP, defined as pulse pressure (PP). After, the best subset of parameters were obtained through the sequential feature selection (SFS) method. According to the results, the best classification was obtained using a combination of linear and nonlinear features from STI and PP parameters. For STI, the best combination was obtained considering the frequency peak and the diagonal structures of RP, with an area under the curve (AUC) of 79%. The same results were obtained when comparing PP values. Consequently, the use of combined linear and nonlinear parameters could improve the risk stratification of cardiomyopathy patients.

JTD Keywords: Feature extraction, Blood pressure, Heart rate, Estimation, Data mining, Covariance matrices, Hospitals

En este trabajo se propone caracterizar la dinámica no-lineal de los sistemas cardíaco, vascular y respiratorio a partir de los diagramas de Poincaré. Se han analizado 46 pacientes con cardiomiopatía isquémica (ICM) o dilatada (DCM), y 35 sujetos sanos. De acuerdo con su fracción de eyección ventricular izquierda (LVEF), los pacientes también fueron clasificados en un grupo de alto riesgo (HR: LVEF ≤ 35%, 30 pacientes) y otro de bajo riesgo (LR: LVEF > 35%, 16 pacientes). A partir de las señales electrocardiográfica, de flujo respiratorio y de presión sanguínea se han obtenido los datos relacionados con el tiempo entre latidos cardíacos (RR), entre valores máximos de presión sistólica (SBP), y la duración del ciclo respiratorio (TTot). Estas series temporales han sido representadas mediante los diagramas de Poincaré, y caracterizadas teniendo en cuenta su desviación a largo plazo (SD1) y su cambio instantáneo (SD2). De acuerdo con los resultados obtenidos, los parámetros de las series cardíaca y de presión sanguínea, relacionados con las diagonales longitudinales y transversales del diagrama de Poincaré, son los que mejor diferencian entre pacientes con HR vs LR. Para la clasificación de pacientes isquémicos vs dilatados, los mejores parámetros se obtuvieron a partir de las series respiratorias y están relacionados con las distancias de la desviación estándar a la línea de identidad. Los cambios en estas relaciones representan una mayor aceleración en la dinámica respiratoria de los pacientes con cardiomiopatía isquémica.

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La retención urinaria después de una cirugía anestésica puede provocar sobre-distensión vesical, impidiendo al paciente miccionar de forma voluntaria. La cateterización es el método más utilizado para solucionar este problema. El método es aplicado cuando el volumen vesical es superior a 300 ml. En este trabajo propone un método para la binarización y segmentación del nodo vesical a partir de una imagen ecográfica de la región suprapúbica transversal. Se han analizado 180 imágenes (80 de entrenamiento y 100 de validación), segmentadas utilizando el método de nivel de gris. Las imágenes fueron caracterizadas a partir de las líneas vertical, horizontal y las dos diagonales. Los valores obtenidos fueron comparados con los medidos con el ecógrafo, con un 72% de acierto. Se ha propuesto un método radial para el cierre de aperturas lateral e interior. Ajustados el brillo y la profundidad de la imagen, y el control morfológico se obtuvo hasta un 83% de correcta segmentación del área vesical en el grupo validación de la muestra. Estos resultados son la base para el cálculo del volumen de orina en vejiga y la decisión de cateterizar un paciente con retención urinaria.

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Obstructive Sleep Apnea (OSA) is a sleep disorder highly prevalent in the general population. Cardiorespiratory Phase Synchronization (CRPS) is a form of non-linear interaction between respiratory and cardiovascular systems that was found to be reduced in severe OSA patients. The Hilbert Transform (HT) method was the recommended choice for estimating the respiratory phase in CRPS studies. But we have noticed that HT provides a phase that is aligned to the transition between the exhalation and the inhalation parts of different breathing cycles, instead of being aligned to the breathing onsets. In this work we proposed a Realigned HT phase estimation method (RHT) and we compared it to the conventional HT and to the Linear Phase (LP) approximation for estimating CRPS in a database of 28 patients with different OSA severity levels. RHT provided similar synchronization percentages (%Sync) as HT, and it enhanced the significant differences in %Sync between mild and severe OSA patients. %Sync showed the highest negative correlation with the Apnea-Hypopnea Index (AHI) when using RHT (rAHI=-0.692, p<;0.001), which only had an 10% extra computational cost. On the other hand, LP method significantly overestimated %Sync especially in the more severe patients, because it was unable to track the phase non-linearities that can be observed during sleep disordered breathing. Therefore, the newly proposed RHT can be the preferred alternative over the conventional HT or the LP approximation for estimating CRPS in OSA patients.

JTD Keywords: Correlation, Databases, Electrocardiography, Phase estimation, Sleep apnea, Synchronization, Transforms

This work investigates the performance of cardiorespiratory analysis detecting periodic breathing (PB) in chest wall recordings in mountaineers climbing to extreme altitude. The breathing patterns of 34 mountaineers were monitored unobtrusively by inductance plethysmography, ECG and pulse oximetry using a portable recorder during climbs at altitudes between 4497 and 7546 m on Mt. Muztagh Ata. The minute ventilation (VE) and heart rate (HR) signals were studied, to identify visually scored PB, applying time-varying spectral, coherence and entropy analysis. In 411 climbing periods, 30–120 min in duration, high values of mean power (MPVE) and slope (MSlopeVE) of the modulation frequency band of VE, accurately identified PB, with an area under the ROC curve of 88 and 89 %, respectively. Prolonged stay at altitude was associated with an increase in PB. During PB episodes, higher peak power of ventilatory (MPVE) and cardiac (MP LF HR ) oscillations and cardiorespiratory coherence (MP LF Coher ), but reduced ventilation entropy (SampEnVE), was observed. Therefore, the characterization of cardiorespiratory dynamics by the analysis of VE and HR signals accurately identifies PB and effects of altitude acclimatization, providing promising tools for investigating physiologic effects of environmental exposures and diseases.

JTD Keywords: High-altitude periodic breathing, Cardiorespiratory characterization, Time-varying spectral analysis, Acclimatization, Hypoxia

Weaning from mechanical ventilation is still one of the most challenging problems in intensive care. Unnecessary delays in discontinuation and weaning trials that are undertaken too early are both undesirable. This study investigated the contribution of spectral signals of heart rate variability (HRV) and respiratory flow, and their coherence to classifying patients on weaning process from mechanical ventilation. A total of 121 candidates for weaning, undergoing spontaneous breathing tests, were analyzed: 73 were successfully weaned (GSucc), 33 failed to maintain spontaneous breathing so were reconnected (GFail), and 15 were extubated after the test but reintubated within 48 h (GRein). The power spectral density and magnitude squared coherence (MSC) of HRV and respiratory flow signals were estimated. Dimensionality reduction was performed using principal component analysis (PCA) and sequential floating feature selection. The patients were classified using a fuzzy K-nearest neighbour method. PCA of the MSC gave the best classification with the highest accuracy of 92% classifying GSucc versus GFail patients, and 86% classifying GSucc versus GRein patients. PCA of the respiratory flow signal gave the best classification between GFail and GRein patients (79% accuracy). These classifiers showed a good balance between sensitivity and specificity. Besides, the spectral coherence between HRV and the respiratory flow signal, in patients on weaning trial process, can contribute to the extubation decision.

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Las enfermedades cardiovasculares son una de las principales causas de muerte en países desarrollados. Se han analizado 42 pacientes con cardiomiopatía isquémica (ICM) o dilatada (DCM), clasificados en función de la fracción de eyección ventricular izquierda (LVEF), en grupos de alto riesgo (HR: LVEF

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Cardiovascular diseases are the first cause of death in developed countries. Using electrocardiographic (ECG), blood pressure (BP) and respiratory flow signals, we obtained parameters for classifying cardiomyophaty patients. 42 patients with ischemic (ICM) and dilated (DCM) cardiomyophaties were studied. The left ventricular ejection fraction (LVEF) was used to stratify patients with low risk (LR: LVEF>35%, 14 patients) and high risk (HR: LVEF≤ 35%, 28 patients) of heart attack. RR, SBP and T_Tot time series were extracted from the ECG, BP and respiratory flow signals, respectively. The time series were transformed to a binary space and then analyzed using Joint Symbolic Dynamic with a word length of three, characterizing them by the probability of occurrence of the words. Extracted parameters were then reduced using correlation and statistical analysis. Principal component analysis and support vector machines methods were applied to characterize the cardiorespiratory and cardiovascular interactions in ICM and DCM cardiomyopaties, obtaining an accuracy of 85.7%.

JTD Keywords: Blood pressure, Electrocardiography, Joints, Kernel, Principal component analysis, Support vector machines, Time series analysis

Cardiorespiratory Phase Synchronization (CRPS) is a manifestation of coupling between cardiac and respiratory systems complementary to Respiratory Sinus Arrhythmia. In this work, we investigated CRPS during wake and sleep stages in Polysomnographic (PSG) recordings of 30 subjects suspected from Obstructive Sleep Apnea (OSA). The population was classified into three severity groups according to the Apnea Hypopnea Index (AHI): G1 (AHI<;15), G2 (15<;=AHI<;30) and G3 (AHI>30). The synchrogram between single lead ECG and respiratory abdominal band signals from PSG was computed with the Hilbert transform technique. The different phase locking ratios (PLR) m:n were monitored throughout the night. Ratio 4:1 was the most frequent and it became more dominant as OSA severity increased. CRPS was characterized by the percentage of synchronized time (%Sync) and the average duration of synchronized epochs (AvDurSync) using three different thresholds. Globally, we observed that %Sync significantly decreased and AvDurSync slightly increased with OSA severity. A high synchronization threshold enhanced these population differences. %Sync was significantly higher in NREM than in REM sleep in G2 and G3 groups. Population differences observed during sleep did not translate to the initial wake state. Reduced CRPS could be an early marker of OSA severity during sleep, but further studies are needed to determine whether CRPS is also present during wakefulness.

JTD Keywords: Band-pass filters, Electrocardiography, Heart beat, Sleep apnea, Sociology, Statistics, Synchronization

Due to the increasing elderly population and the extensive number of comorbidities that affect them, studies are required to determine future increments in admission to emergency departments. Some of these studies could focus on the relation between chronic diseases and breathing pattern in elderly patients. Variations in the fractal properties of respiratory signals can be associated with several diseases. To determine the relationship between these variations and breathing patterns, and to quantify the fractal properties of respiratory flow signals, we estimated the Hurst exponent (H). Detrended fluctuation analysis (DFA) and discrete wavelet transform-based estimation (DWTE) methods were applied. The estimation methods were analyzed using simulated data series generated by fractional Gaussian noise. 43 elderly patients (19 patients with a non-periodic breathing pattern - nPB, and 24 patients with a periodic breathing pattern - PB) were studied. The results were evaluated according to the length of data and the number of averaged data series used to obtain a good estimation. The DWTE method estimated the respiratory flow signals better than the DFA method, and obtained Hurst values clustered by group. We found significant differences in the H exponent (p = 0.002) between PB and nPB patients, which showed different behavior in the fractal properties.

JTD Keywords: Discrete wavelet transforms, Diseases, Estimation, Fractals, Modulation, Senior citizens, Time series analysis

In this work, the flow signal Expiratory Pause (EP) temporal analysis is used in 18 patients with obstructive lung diseases going through spontaneous breathing trial at weaning process. The main objective was to identify the patients who were successfully disconnected (success group: 9 patients), and those who were not (failure and reintubated group: 9 patients). A variable selection stage was done by mean group comparison and step wise variable inclusion, leading to a 3 parameters set: EP time median; cycle time mean; and median absolute deviation of the EP maxima local number. Next, this set was used in a classifier based on linear discriminant analysis, which results in 17 patients (94.4%) correctly classified, with 88.9% of specificity (Sp) and 100% of sensitivity (Se). Finally, applying the leave-one-out cross validation method, results were 88.9% of correctly classified patients (Sp=77.8% and Se=100%). In conclusion, the proposed parameters showed a good performance and could be used to help therapists to wean patients with obstructive diseases.

JTD Keywords: Chronic Obstructive Pulmonary Disease (COPD), Weaning, Mechanical ventilation, Expiratory pause

La extubación de pacientes asistidos mediante ventilación mecánica sigue siendo un proceso fundamental en la práctica clínica, de especial atención en las unidades de cuidados intensivos. En este estudio se propone un nuevo índice de extubación basado en la potencia de la señal de flujo respiratorio (Pi). Se estudiaron un total de 132 pacientes sometidos al proceso de destete: 94 pacientes (GE) con resultado de éxito en la prueba, y 38 pacientes (GF) que fracasaron en el proceso de destete y tuvieron que ser deconectados al ventilador mecánico. La señal de flujo respiratorio fue procesada para obtener la potencia de la fase inspiratoria, considerando las siguientes etapas: a) detección del cruce por cero, b) detección del punto de inflexión, y c) obtención de la potencia de la señal hasta dicho punto. La detección de cruce por cero se realizó utilizando un algoritmo basado en umbrales. Los puntos de inflexión fueron marcados teniendo en cuenta el cero de la segunda derivada. La potencia de la fase inspiratoria se calculó a partir de la energía de la señal desde el cruce por cero hasta el punto de máxima inflexión. El nuevo índice fue evaluado como estimador de éxito en la extubación. Los resultados fueron analizados utilizando clasificadores como regresión logística, análisis discriminante lineal, árboles de decisión, teoría bayesiana, y máquinas de soporte vectorial. Los clasificadores Bayesianos presentaron los mejores resultados con una exactitud del 87%, y sensibilidad y especificidad de 90% y 81%, respectivamente.

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La clínica relacionada con los pacientes ancianos, refleja una elevada incidencia de enfermedades de origen cardíaco y respiratorio. Alteraciones en el patrón respiratorio como son la respiración periódica (PB) y la respiración Cheyne-Stokes (CSR) pueden estar relacionadas con la insuficiencia cardíaca crónica (ICC). En este estudio se propuso caracterizar estos patrones respiratorios a partir de la envolvente de la señal de flujo respiratorio, aplicando técnicas de análisis frecuencial y de tiempo-frecuencia. Se estudiaron registros de 45 pacientes ancianos (25 pacientes con patrón PB y 20 pacientes con respiración no periódica (nPB)). Se analizaron los resultados considerando todas las posibles combinaciones de tipos de patrones: pacientes con patrones PB (con y sin apnea) vs nPB, y patrones CSR vs PB, CSR vs nPB y PB vs nPB. En el análisis tiempo-frecuencia se obtuvo la mayor exactitud (76.3%) con parámetros correspondientes a la variabilidad frecuencial y la desviación del pico de potencia, al comparar pacientes con patrón respiratorio nPB vs PB. Considerando segmentos de señal de 5 minutos, la potencia de pico de modulación, la variabilidad frecuencial y los rangos intercuartílicos presentaron los mejores resultados, con una exactitud del 72.8% al comparar los tres grupos (nPB, PB y CSR), y del 74.2% al comparar patrones PB vs nPB.

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Disconnection from mechanical ventilation, called the weaning process, is an additional difficulty in the management of patients in intensive care units (ICU). Unnecessary delays in the discontinuation process and a weaning trial that is undertaken too early are undesirable. In this study, we propose an extubation index based on the power of the respiratory flow signal (Pi). A total of 132 patients on weaning trials were studied: 94 patients with successful trials (group S) and 38 patients who failed to maintain spontaneous breathing and were reconnected (group F). The respiratory flow signals were processed considering the following three stages: a) zero crossing detection of the inspiratory phase, b) inflection point detection of the flow curve during the inspiratory phase, and c) calculation of the signal power on the time instant indicated by the inflection point. The zero crossing detection was performed using an algorithm based on thresholds. The inflection points were marked considering the zero crossing of the second derivative. Finally, the inspiratory power was calculated from the energy contained over the finite time interval (between the instant of zero crossing and the inflection point). The performance of this parameter was evaluated using the following classifiers: logistic regression, linear discriminant analysis, the classification and regression tree, Naive Bayes, and the support vector machine. The best results were obtained using the Bayesian classifier, which had an accuracy, sensitivity and specificity of 87%, 90% and 81% respectively.

JTD Keywords: Bayes methods, Bayesian classifier, Indexes, Logistics, Niobium, Regression tree analysis, Support vector machines, Ventilation

Blood pressure carries a great deal of information about people’s physical attributes. We analyzed the blood pressure signal in healthy subjects considering two positions, supine and sitting. 44 healthy subjects were studied. Parameters extracted from the blood pressure signal, related to time and frequency domain were used to compare the effect of postural position between supine and sitting. In time domain analysis, the time systolic interval and the time of blood pressure interval were higher in supine than in sitting position (p = 0.001 in both case). Parameters related to frequency peak, interquartile range, in frequency domain presented statistically significant difference (p < 0.0005 in both case). The blood pressure variability parameters presented smaller values in supine than in sitting position (p < 0.0005). In general, the position change of supine to sitting produces an increment in the pressure gradient inside heart, reflected in the blood pressure variability.

JTD Keywords: Blood pressure variability, Systolic time intervals, Diastolic time intervals

The determination of the optimal time of the patients in weaning trial process from mechanical ventilation, between patients capable of maintaining spontaneous breathing and patients that fail to maintain spontaneous breathing, is a very important task in intensive care unit. Wavelet Transform (WT) and Neural Networks (NN) techniques were applied in order to develop a classifier for the study of patients on weaning trial process. The respiratory pattern of each patient was characterized through different time series. Genetic Algorithms (GA) and Forward Selection were used as feature selection techniques. A classification performance of 77.00±0.06% of well classified patients, was obtained using a NN and GA combination, with only 6 variables of the 14 initials.

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Classification algorithms with unbalanced datasets tend to produce high predictive accuracy over the majority class, but poor predictive accuracy over the minority class. This problem is very common in biomedical data mining. This paper introduces a Support Vector Machine (SVM)-based optimized feature selection method, to select the most relevant features and maintain an accurate and well-balanced sensitivity–specificity result between unbalanced groups. A new metric called the balance index (B) is defined to implement this optimization. The balance index measures the difference between the misclassified data within each class. The proposed optimized feature selection is applied to the classification of patients' weaning trials from mechanical ventilation: patients with successful trials who were able to maintain spontaneous breathing after 48 h and patients who failed to maintain spontaneous breathing and were reconnected to mechanical ventilation after 30 min. Patients are characterized through cardiac and respiratory signals, applying joint symbolic dynamic (JSD) analysis to cardiac interbeat and breath durations. First, the most suitable parameters (C+,C−,σ) are selected to define the appropriate SVM. Then, the feature selection process is carried out with this SVM, to maintain B lower than 40%. The best result is obtained using 6 features with an accuracy of 80%, a B of 18.64%, a sensitivity of 74.36% and a specificity of 82.42%.

JTD Keywords: Support vector machines, Balance index, Sensitivity-specificity balance, Cardiorespiratory interaction, Joint symbolic dynamics, Feature selection, Weaning procedure

Assessment of the dynamic interactions between cardiovascular signals can provide valuable information that improves the understanding of cardiovascular control. Heart rate variability (HRV) analysis is known to provide information about the autonomic heart rate modulation mechanism. Using the HRV signal, we aimed to obtain parameters for classifying patients with and without chronic heart failure (CHF), and with periodic breathing (PB), non-periodic breathing (nPB), and Cheyne-Stokes respiration (CSR) patterns. An electrocardiogram (ECG) and a respiratory flow signal were recorded in 36 elderly patients: 18 patients with CHF and 18 patients without CHF. According to the clinical criteria, the patients were classified into the follow groups: 19 patients with nPB pattern, 7 with PB pattern, 4 with Cheyne-Stokes respiration (CSR), and 6 non-classified patients (problems with respiratory signal). From the HRV signal, parameters in the time and frequency domain were calculated. Frequency domain parameters were the most discriminant in comparisons of patients with and without CHF: P_Tot (p = 0.02), P_LF (p = 0.022) and fp_HF (p = 0.021). For the comparison of the nPB vs. CSR patients groups, the best parameters were RMSSD (p = 0.028) and SDSD (p = 0.028). Therefore, the parameters appear to be suitable for enhanced diagnosis of decompensated CHF patients and the possibility of developed periodic breathing and a CSR pattern.

JTD Keywords: cardiovascular system, diseases, electrocardiography, frequency-domain analysis, geriatrics, medical signal processing, patient diagnosis, pneumodynamics, signal classification, Cheyne-Stokes respiration patterns, ECG, autonomic heart rate modulation mechanism, cardiovascular control, cardiovascular signals, chronic heart failure, decompensated CHF patients, dynamic interaction assessment, elderly patients, electrocardiogram, enhanced diagnosis, frequency domain parameters, heart rate variability analysis, patient classification, periodic breathing, respiratory flow signal recording, Electrocardiography, Frequency modulation, Frequency-domain analysis, Heart rate variability, Senior citizens, Standards

Ischemic and dilated cardiomyopathy are associated with disorders of myocardium. Using the blood pressure (BP) signal and the values of the ventricular ejection fraction, we obtained parameters for stratifying cardiomyopathy patients as low- and high-risk. We studied 48 cardiomyopathy patients characterized by NYHA ≥2: 19 patients with dilated cardiomyopathy (DCM) and 29 patients with ischemic cardiomyopathy (ICM). The left ventricular ejection fraction (LVEF) percentage was used to classify patients in low risk (LR: LVEF > 35%, 17 patients) and high risk (HR: LVEF ≤ 35%, 31 patients) groups. From the BP signal, we extracted the upward systolic slope (BP_sl), the difference between systolic and diastolic BP (BPA), and systolic time intervals (STI). When we compared the LR and HR groups in the time domain analysis, the best parameters were standard deviation (SD) of 1=STI, kurtosis (K) of BP_sl, and K of BPA. In the frequency domain analysis, very low frequency (VLF) and high frequency (HF) bands showed statistically significant differences in comaprisons of LR and HR groups. The area under the curve of power spectral density was the best parameter in all classifications, and particularly in the very-low-and high- frequency bands (p <; 0.001). These parameters could help to improve the risk stratification of cardiomyopathy patients.

JTD Keywords: blood pressure measurement, cardiovascular system, diseases, medical disorders, medical signal processing, statistical analysis, time-domain analysis, BP signal, HR groups, LR groups, blood pressure signal analysis, cardiomyopathy patients, diastolic BP, dilated cardiomyopathy, frequency domain analysis, high-frequency bands, ischemic cardiomyopathy, left ventricular ejection fraction, low-frequency bands, myocardium disorders, patient characterization, power spectral density curve, standard deviation, statistical significant differences, systolic BP, systolic slope, systolic time intervals, time domain analysis, ventricular ejection fraction, Abstracts, Databases, Parameter extraction, Telecommunication standards, Time-frequency analysis

One of the most challenging problems in intensive care is still the process of discontinuing mechanical ventilation, called weaning process. Both an unnecessary delay in the discontinuation process and a weaning trial that is undertaken too early are undesirable. In this study, we analyzed respiratory pattern variability using the respiratory volume signal of patients submitted to two different levels of pressure support ventilation (PSV), prior to withdrawal of the mechanical ventilation. In order to characterize the respiratory pattern, we analyzed the following time series: inspiratory time, expiratory time, breath duration, tidal volume, fractional inspiratory time, mean inspiratory flow and rapid shallow breathing. Several autoregressive modeling techniques were considered: autoregressive models (AR), autoregressive moving average models (ARMA), and autoregressive models with exogenous input (ARX). The following classification methods were used: logistic regression (LR), linear discriminant analysis (LDA) and support vector machines (SVM). 20 patients on weaning trials from mechanical ventilation were analyzed. The patients, submitted to two different levels of PSV, were classified as low PSV and high PSV. The variability of the respiratory patterns of these patients were analyzed. The most relevant parameters were extracted using the classifiers methods. The best results were obtained with the interquartile range and the final prediction errors of AR, ARMA and ARX models. An accuracy of 95% (93% sensitivity and 90% specificity) was obtained when the interquartile range of the expiratory time and the breath duration time series were used a LDA model. All classifiers showed a good compromise between sensitivity and specificity.

JTD Keywords: autoregressive moving average processes, feature extraction, medical signal processing, patient care, pneumodynamics, signal classification, support vector machines, time series, ARX, autoregressive modeling techniques, autoregressive models with exogenous input, autoregressive moving average model, breath duration time series, classification method, classifier method, discontinuing mechanical ventilation, expiratory time, feature extraction, final prediction errors, fractional inspiratory time, intensive care, interquartile range, linear discriminant analysis, logistic regression analysis, mean inspiratory flow, patient respiratory volume signal, pressure support level, pressure support ventilation, rapid shallow breathing, respiratory pattern variability characterization, support vector machines, tidal volume, weaning trial, Analytical models, Autoregressive processes, Biological system modeling, Estimation, Support vector machines, Time series analysis, Ventilation

This work investigates the very low frequency (VLF) modulation of QRS slopes and heart rate variability (HRV). Electrocardiogram (ECG) and respiratory flow signal were acquired from patients with dilated cardiomyopathy and ischemic cardiomyopathy. HRV as well as the upward QRS slope (I_US) and downward QRS slope (I_DS) were extracted from the ECG. The relation between HRV and QRS slopes in the VLF band was measured using ordinary coherence in 5-minute segments. Partial coherence was then used to remove the influence that respiration simultaneously exerts on HRV and QRS slopes. A statistical threshold was determined, below which coherence values were considered not to represent a linear relation. 7 out of 276 segments belonging to 5 out of 29 patients for I_US and 10 segments belonging to 5 patients for I_DS presented a VLF modulation in QRS slopes, HRV and respiration. In these segments spectral coherence was statistically significant, while partial coherence decreased, indicating that the coupling HRV and QRS slopes was related to respiration. 4 segments had a partial coherence value below the threshold for I_US, 3 segments for I_DS. The rest of the segments also presented a notable decrease in partial coherence, but still above the threshold, which means that other non-linearly effects may also affect this modulation.

JTD Keywords: diseases, electrocardiography, feature extraction, medical signal processing, pneumodynamics, statistical analysis, ECG, QRS slopes, cardiomyopathy patients, dilated cardiomyopathy, electrocardiogram, feature extraction, heart rate variability, ischemic cardiomyopathy, ordinary coherence, partial coherence value, respiration, respiratory flow signal acquisition, spectral coherence, statistical threshold, time 5 min, very low frequency modulation, Coherence, Educational institutions, Electrocardiography, Frequency modulation, Heart rate variability

The process of weaning from mechanical ventilation is one of the challenges in intensive care units. 66 patients under extubation process (T-tube test) were studied: 33 patients with successful trials and 33 patients who failed to maintain spontaneous breathing and were reconnected. Each patient was characterized using 7 time series from respiratory signals, and for each serie was evaluated the discrete wavelet transform. It trains a neural network for discriminating between patients from the two groups.

JTD Keywords: discrete wavelet transforms, neural nets, patient treatment, pneumodynamics, time series, ventilation, T-tube test, discrete wavelet transform, extubation process, intensive care units, mechanical ventilation, moment of disconnection, neural network, patients, respiratory signals, spontaneous breathing, time series, weaning, Mechanical Ventilation, Neural Networks, Time series from respiratory signals, Wavelet Transform

Some of the most common clinical problems in elderly patients are related to diseases of the cardiac and respiratory systems. Elderly patients often have altered breathing patterns, such as periodic breathing (PB) and Cheyne-Stokes respiration (CSR), which may coincide with chronic heart failure. In this study, we used the envelope of the respiratory flow signal to characterize respiratory patterns in elderly patients. To study different breathing patterns in the same patient, the signals were segmented into windows of 5 min. In oscillatory breathing patterns, frequency and time-frequency parameters that characterize the discriminant band were evaluated to identify periodic and non-periodic breathing (PB and nPB). In order to evaluate the accuracy of this characterization, we used a feature selection process, followed by linear discriminant analysis. 22 elderly patients (7 patients with PB and 15 with nPB pattern) were studied. The following classification problems were analyzed: patients with either PB (with and without apnea) or nPB patterns, and patients with CSR versus PB, CSR versus nPB and PB versus nPB patterns. The results showed 81.8% accuracy in the comparisons of nPB and PB patients, using the power of the modulation peak. For the segmented signal, the power of the modulation peak, the frequency variability and the interquartile ranges provided the best results with 84.8% accuracy, for classifying nPB and PB patients.

JTD Keywords: cardiovascular system, diseases, feature extraction, geriatrics, medical signal processing, oscillations, pneumodynamics, signal classification, time-frequency analysis, Cheyne-Stokes respiration, apnea, cardiac systems, chronic heart failure, classification problems, discriminant band, diseases, elderly patients, feature selection process, frequency variability, interquartile ranges, linear discriminant analysis, nonperiodic breathing, oscillatory breathing pattern, periodic breathing, respiratory How signal, respiratory systems, signal segmentation, time 5 min, time-frequency parameters, Accuracy, Aging, Frequency modulation, Heart, Senior citizens, Time-frequency analysis

One objective of mechanical ventilation is the recovery of spontaneous breathing as soon as possible. Remove the mechanical ventilation is sometimes more difficult that maintain it. This paper proposes the study of respiratory flow signal of patients on weaning trials process by autoregressive moving average model (ARMA), through the location of poles and zeros of the model. A total of 151 patients under extubation process (T-tube test) were analyzed: 91 patients with successful weaning (GS), 39 patients that failed to maintain spontaneous breathing and were reconnected (GF), and 21 patients extubated after the test but before 48 hours were reintubated (GR). The optimal model was obtained with order 8, and statistical significant differences were obtained considering the values of angles of the first four poles and the first zero. The best classification was obtained between GF and GR, with an accuracy of 75.3% on the mean value of the angle of the first pole.

JTD Keywords: Analytical models, Biological system modeling, Computational modeling, Estimation, Hospitals, Poles and zeros, Ventilation, Autoregressive moving average processes, Patient care, Patient monitoring, Pneumodynamics, Poles and zeros, Ventilation, ARMA model, T-tube test, Autoregressive moving average model, Extubation process, Mechanical ventilation, Optimal model, Patient classification, Respiratory flow signal, Roots, Spontaneous breathing, Weaning trials

In this paper, the problem of time alignment is revisited by adopting an ensemble-based approach with all signals jointly aligned. It is shown that the maximization of an eigenvalue ratio is synonymous to maximizing the signal-to-jitter-and-noise ratio. Since optimization of this criterion is extremely time consuming, a relaxed optimization procedure is introduced which converges much more quickly. Using simulations based on respiratory flow signals, the results suggest that the time delay error variance of the new method is much lower than that obtained with the well-known Woody’s method.

JTD Keywords: Time alignment, Signal ensemble, Subsample precision, Eigenvalue decomposition

La respiración periódica (PB) a gran altitud comparte aspectos fisiopatológicos con la apnea, la respiración Cheyne-Stokes y la PB en pacientes con insuficiencia cardiaca. Cuantificar las inestabilidades del control respiratorio puede proporcionar información relevante de los mecanismos fisiológicos que las producen, y ayudar en las actuaciones terapéuticas. Bajo la hipótesis de que en altitudes extremas la PB puede aparecer incluso durante actividad física, el objetivo es identificar la PB y evaluar el efecto de aclimatación a partir de la caracterización del patrón respiratorio mediante la señal de volumen respiratorio. Se analizaron los datos obtenidos de 34 montañeros sanos ascendiendo al Muztagh Ata, China (7,546m). Sus señales se etiquetaron visualmente como, respiración periódica (PB=40) y no periódica (nPB=371). El patrón respiratorio se caracterizó a partir de parámetros extraídos de la densidad espectral de potencia de la señal de volumen respiratorio. Los mejores resultados clasificando PB y nPB se obtuvieron con Pm (potencia de modulación) y R (ratio entre potencia de modulación y respiración) con una precisión del 80.3% y un área bajo la curva de 84.5%. SaO2 y el número de ciclos periódicos de respiración aumentaron significativamente con la aclimatación (p-valor<0.05). A menor SaO2 se observó una mayor Pm y frecuencia respiratoria, (correlación negativa, p-valor<0.01), y una mayor Pm en periodos etiquetados como PB con > 5 ciclos respiratorios periódicos, (correlación positiva, p-valor<0.01). Estos resultados demuestran que la caracterización espectral de la señal de volumen respiratorio permite identificar los efectos de la hipoxia hipobárica en el control de la respiración.

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Weaning trials process of patients in intensive care units is a complex clinical procedure. 153 patients under extubation process (T-tube test) were studied: 94 patients with successful trials (group S), 38 patients who failed to maintain spontaneous breathing and were reconnected (group F), and 21 patients with successful test but that had to be reintubated before 48 hours (group R). The respiratory pattern of each patient was characterized through the following time series: inspiratory time (T_I), expiratory time (T_E), breathing cycle duration (T_Tot), tidal volume (V_T), inspiratory fraction (T_I/T_Tot), half inspired flow (V_T/T_I), and rapid shallow index (f/V_T), where f is respiratory rate. Using techniques as autoregressive models (AR), autoregressive moving average models (ARMA) and autoregressive models with exogenous input (ARX), the most relevant parameters of the respiratory pattern were obtained. We proposed the evaluation of these parameters using classifiers as logistic regression (LR), linear discriminant analysis (LDA), support vector machines (SVM) and classification and regression tree (CART) to discriminate between patients from groups S, F and R. An accuracy of 93% (98% sensitivity and 82% specificity) has been obtained using CART classification.

JTD Keywords: Accuracy, Indexes, Logistics, Regression tree analysis, Support vector machines, Time series analysis, Autoregressive moving average processes, Medical signal processing, Pattern classification, Pneumodynamics, Regression analysis, Sensitivity, Signal classification, Support vector machines, Time series, SVM, T-tube testing, Autoregressive models-with-exogenous input, Autoregressive moving average models, Breathing cycle duration, Classification-and-regression tree, Expiratory time, Extubation process, Half inspired flow, Inspiratory fraction, Inspiratory time, Intensive care units, Linear discriminant analysis, Logistic regression, Rapid shallow index, Respiratory pattern parameter performance, Sensitivity, Spontaneous breathing, Support vector machines, Tidal volume, Time 48 hr, Time series, Weaning process classifiers

High altitude periodic breathing (PB) shares some common pathophysiologic aspects with sleep apnea, Cheyne-Stokes respiration and PB in heart failure patients. Methods that allow quantifying instabilities of respiratory control provide valuable insights in physiologic mechanisms and help to identify therapeutic targets. Under the hypothesis that high altitude PB appears even during physical activity and can be identified in comparison to visual analysis in conditions of low SNR, this study aims to identify PB by characterizing the respiratory pattern through the respiratory volume signal. A number of spectral parameters are extracted from the power spectral density (PSD) of the volume signal, derived from respiratory inductive plethysmography and evaluated through a linear discriminant analysis. A dataset of 34 healthy mountaineers ascending to Mt. Muztagh Ata, China (7,546 m) visually labeled as PB and non periodic breathing (nPB) is analyzed. All climbing periods within all the ascents are considered (total climbing periods: 371 nPB and 40 PB). The best crossvalidated result classifying PB and nPB is obtained with Pm (power of the modulation frequency band) and R (ratio between modulation and respiration power) with an accuracy of 80.3% and area under the receiver operating characteristic curve of 84.5%. Comparing the subjects from 1_st and 2_nd ascents (at the same altitudes but the latter more acclimatized) the effect of acclimatization is evaluated. SaO₂ and periodic breathing cycles significantly increased with acclimatization (p-value <; 0.05). Higher Pm and higher respiratory frequencies are observed at lower SaO₂, through a significant negative correlation (p-value <; 0.01). Higher Pm is observed at climbing periods visually labeled as PB with >; 5 periodic breathing cycles through a significant positive correlation (p-value <; 0.01). Our data demonstrate that quantification of the respiratory volum- signal using spectral analysis is suitable to identify effects of hypobaric hypoxia on control of breathing.

JTD Keywords: Frequency domain analysis, Frequency modulation, Heart, Sleep apnea, Ventilation, Visualization, Cardiology, Medical disorders, Medical signal processing, Plethysmography, Pneumodynamics, Sensitivity analysis, Sleep, Spectral analysis, Cheyne-Stokes respiration, Climbing periods, Dataset, Heart failure patients, High altitude PB, High altitude periodic breathing, Hypobaric hypoxia, Linear discriminant analysis, Pathophysiologic aspects, Physical activity, Physiologic mechanisms, Power spectral density, Receiver operating characteristic curve, Respiratory control, Respiratory frequency, Respiratory inductive plethysmography, Respiratory pattern, Respiratory volume signal, Sleep apnea, Spectral analysis, Spectral parameters

The study of flow cycle morphology provides new information about the breathing pattern. This study proposes the characterization of cycle morphology in chronic heart failure patients (CHF) patients, with periodic (PB) and non-periodic breathing (nPB) patterns, and healthy subjects. Principal component analysis is applied to extract a respiratory cycle model for each time segment defined by a 30-s moving window. To characterize morphology of the model waveform, a number of parameters are extracted whose significance is evaluated in terms of the following three classification problems: CHF patients with either PB or nPB, CHF patients versus healthy subjects, and nPB patients versus healthy subjects. 26 CHF patients (8 with PB and 18 with non-periodic breathing pattern (nPB)) and 35 healthy subjects are studied. The results show that a respiratory cycle compressed in time characterizes PB patients, i.e., shorter inspiratory and expiratory periods, and higher dispersion of the maximum inspiratory and expiratory flow value (accuracy of 87%). The maximal expiratory flow instant occurs earlier in CHF patients than in healthy subjects (accuracy of 87%), with a steeper slope between inspiration and expiration. It is also found that the standard deviation of the expiratory period, evaluated for each subject, is much lower in CHF patients than in healthy subjects. The maximal expiratory flow instant occurs earlier (accuracy of 84%) in nPB patients, when comparing subjects with similar respiratory pattern like nPB patients and healthy subjects.

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One of the most challenging problems in intensive care is the process of discontinuing mechanical ventilation, called weaning process. An unnecessary delay in the discontinuation process and an early weaning trial are undesirable. This paper proposes to analysis the respiratory pattern variability of these patients using autoregressive modeling techniques: autoregressive models (AR), autoregressive moving average models (ARMA), and autoregressive models with exogenous input (ARX). A total of 153 patients on weaning trials from mechanical ventilation were analyzed: 94 patients with successful weaning (group S); 38 patients that failed to maintain spontaneous breathing (group F), and 21 patients who had successful weaning trials, but required reintubation in less than 48 h (group R). The respiratory pattern was characterized by their time series. The results show that significant differences were obtained with parameters as model order and first coefficient of AR model, and final prediction error by ARMA model. An accuracy of 86% (84% sensitivity and 86% specificity) has been obtained when using order model and first coefficient of AR model, and mean of breathing duration.

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Autonomic nervous system regulates the behavior of cardiac and respiratory systems. Its assessment during the ventilator weaning can provide information about physio-pathological imbalances. This work proposes a non linear analysis of the complexity of the heart rate variability (HRV) and breathing duration (TTot) applying recurrence plot (RP) and their interaction joint recurrence plot (JRP). A total of 131 patients on weaning trials from mechanical ventilation were analyzed: 92 patients with successful weaning (group S) and 39 patients that failed to maintain spontaneous breathing (group F). The results show that parameters as determinism (DET), average diagonal line length (L), and entropy (ENTR), are statistically significant with RP for TTot series, but not with HRV. When comparing the groups with JRP, all parameters have been relevant. In all cases, mean values of recurrence quantification analysis are higher in the group S than in the group F. The main differences between groups were found on the diagonal and vertical structures of the joint recurrence plot.

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This study proposes a method for the characterization of respiratory patterns in chronic heart failure (CHF) patients with periodic breathing (PB) and nonperiodic breathing (nPB), using the flow signal. Autoregressive modeling of the envelope of the respiratory flow signal is the starting point for the pattern characterization. Spectral parameters extracted from the discriminant frequency band (DB) are used to characterize the respiratory patterns. For each classification problem, the most discriminant parameter subset is selected using the leave-one-out cross-validation technique. The power in the right DB provides an accuracy of 84.6% when classifying PB vs. nPB patterns in CHF patients, whereas the power of the DB provides an accuracy of 85.5% when classifying the whole group of CHF patients vs. healthy subjects, and 85.2% when classifying nPB patients vs. healthy subjects.

JTD Keywords: Chronic heart failure, AR modeling, Respiratory pattern, Discriminant band, Periodic and nonperiodic breathing

Traditional time-domain techniques of data analysis are often not sufficient to characterize the complex dynamics of the cardiorespiratory interdependencies during the weaning trials. In this paper, the interactions between the heart rate (HR) and the breathing rate (BR) were studied using joint symbolic dynamic analysis. A total of 133 patients on weaning trials from mechanical ventilation were analyzed: 94 patients with successful weaning (group S) and 39 patients that failed to maintain spontaneous breathing (group F). The word distribution matrix enabled a coarse-grained quantitative assessment of short-term nonlinear analysis of the cardiorespiratory interactions. The histogram of the occurrence probability of the cardiorespiratory words presented a higher homogeneity in group F than in group S, measured with a higher number of forbidden words in group S as well as a higher number of words whose probability of occurrence is higher than a probability threshold in group S. The discriminant analysis revealed the best results when applying symbolic dynamic variables. Therefore, we hypothesize that joint symbolic dynamic analysis provides enhanced information about different interactions between HR and BR, when comparing patients with successful weaning and patients that failed to maintain spontaneous breathing in the weaning procedure.

JTD Keywords: Dynamical nonlinearities analysis, Cardiorespiratory interdependencies, Joint symbolic dynamic, Weaning procedure

A correntropy-based technique is proposed for the characterization and classification of respiratory flow signals in chronic heart failure (CHF) patients with periodic or nonperiodic breathing (PB or nPB, respectively) and healthy subjects. The correntropy is a recently introduced, generalized correlation measure whose properties lend themselves to the definition of a correntropy-based spectral density (CSD). Using this technique, both respiratory and modulation frequencies can be reliably detected at their original positions in the spectrum without prior demodulation of the flow signal. Single-parameter classification of respiratory patterns is investigated for three different parameters extracted from the respiratory and modulation frequency bands of the CSD, and one parameter defined by the correntropy mean. The results show that the ratio between the powers in the modulation and respiratory frequency bands provides the best result when classifying CHF patients with either PB or nPB, yielding an accuracy of 88.9%. The correntropy mean offers excellent performance when classifying CHF patients versus healthy subjects, yielding an accuracy of 95.2% and discriminating nPB patients from healthy subjects with an accuracy of 94.4%.

JTD Keywords: Autoregressive (AR) modeling, Chronic heart failure (CHF), Correntropy spectral density (CSD), Linear classification, Periodic breathing (PB)

The process of discontinuing mechanical ventilation is called weaning and is one of the most challenging problems in intensive care. An unnecessary delay in the discontinuation process and an early weaning trial are undesirable. This study aims to characterize the respiratory pattern through features that permit the identification of patients' conditions in weaning trials. Three groups of patients have been considered: 94 patients with successful weaning trials, who could maintain spontaneous breathing after 48 h ( GSucc ); 39 patients who failed the weaning trial ( GFail ) and 21 patients who had successful weaning trials, but required reintubation in less than 48 h ( GRein ). Patients are characterized by their cardiorespiratory interactions, which are described by joint symbolic dynamics (JSD) applied to the cardiac interbeat and breath durations. The most discriminating features in the classification of the different groups of patients ( GSucc , GFail and GRein ) are identified by support vector machines (SVMs). The SVM-based feature selection algorithm has an accuracy of 81% in classifying GSucc versus the rest of the patients, 83% in classifying GRein versus GSucc patients and 81% in classifying GRein versus the rest of the patients. Moreover, a good balance between sensitivity and specificity is achieved in all classifications.

JTD Keywords: Mechanical ventilation, Weaning, Support vector machines, Joint symbolic dynamics

In this study we propose the correntropy function as a discriminative measure for detecting nonlinearities in the respiratory pattern of chronic heart failure (CHF) patients with periodic or nonperiodic breathing pattern (PB or nPB, respectively). The complexity seems to be reduced in CHF patients with higher risk level. Correntropy reflects information on both, statistical distribution and temporal structure of the underlying dataset. It is a suitable measure due to its capability to preserve nonlinear information. The null hypothesis considered is that the analyzed data is generated by a Gaussian linear stochastic process. Correntropy is used in a statistical test to reject the null hypothesis through surrogate data methods. Various parameters, derived from the correntropy and correntropy spectral density (CSD) to characterize the respiratory pattern, presented no significant differences when extracted from the iteratively refined amplitude adjusted Fourier transform (IAAFT) surrogate data. The ratio between the powers in the modulation and respiratory frequency bands R was significantly different in nPB patients, but not in PB patients, which reflects a higher presence of nonlinearities in nPB patients than in PB patients.

JTD Keywords: Practical, Theoretical or Mathematical, Experimental/cardiology diseases, Fourier transforms, Medical signal processing, Pattern classification, Pneumodynamics, Spectral analysis, Statistical analysis, Stochastic processes/ correntropy based nonlinearity test, Chronic heart failure, Correntropy function, Respiratory pattern nonlinearities, CHF patients, Nonperiodic breathing pattern, Dataset statistical distribution, Dataset temporal structure, Nonlinear information, Null hypothesis, Gaussian linear stochastic process, Statistical test, Correntropy spectral density, Iteratively refined amplitude adjusted Fourier transform, Surrogate data, Periodic breathing pattern

Statistical analysis, power spectral density, and Lempel Ziv complexity, are used in a multi-parameter approach to analyze four temporal series obtained from the Electrocardiographic and Respiratory Flow signals of 126 patients on weaning trials. In which, 88 patients belong to successful group (SG), and 38 patients belong to failure group (FG), i.e. failed to maintain spontaneous breathing during trial. It was found that mean values of cardiac inter-beat and breath durations give higher values for SG than for FG; Kurtosis coefficient of the spectrum of the rapid shallow breathing index is higher for FG; also Lempel Ziv complexity mean values associated with the respiratory flow signal are bigger for FG. Patients were then classified with a pattern recognition neural network, obtaining 80% of correct classifications (81.6% for FG and 79.5% for SG).

JTD Keywords: Electrocardiography, Medical signal processing, Neural nets, Pattern recognition, Pneumodynamics, Signal classification, Statistical analysis, ECG, Kurtosis coefficient, Lempel Ziv complexity, Breath durations, Cardiac interbeat durations, Electrocardiography, Multiparameter analysis, Pattern recognition neural network, Power spectral density, Respiratory flow signals, Signal classification, Spontaneous breathing, Statistical analysis, Weaning trials

A considerable number of patients in weaning process have problems to keep spontaneous breathing during the trial and after it. This study proposes to extract characteristic parameters of the RR series and respiratory flow signal according to the patients' condition in weaning test. Three groups of patients have been considered: 93 patients with successful trials (group S), 40 patients that failed to maintain spontaneous breathing (group F), and 21 patients who had successful weaning trials, but that had to be reintubated before 48 hours (group R). The characterization was performed using spectral analysis of the signals, through the power spectral density, cross power spectral density and Coherence method. The parameters were extracted on the three frequency bands (VLF, LF and HF), and the principal statistical differences between groups were obtained in bands of VLF and HF. The results show an accuracy of 76.9% in the classification of the groups S and F.

JTD Keywords: Biomedical measurement, Electrocardiography, Medical signal processing, Pneumodynamics, Spectral analysis, RR series, Coherence method, Cross power spectral density, Electrocardiography, Principal statistical differences, Respiratory flow signal, Spectral analysis, Spontaneous breathing, Weaning test

A correntropy-based technique is proposed for the analysis and characterization of respiratory flow signals in chronic heart failure (CHF) patients with both periodic and nonperiodic breathing (PB and nPB), and healthy subjects. Correntropy is a novel similarity measure which provides information on temporal structure and statistical distribution simultaneously. Its properties lend itself to the definition of the correntropy spectral density (CSD). An interesting result from CSD-based spectral analysis is that both the respiratory frequency and modulation frequency can be detected at their original positions in the spectrum without prior demodulation of the flow signal. The respiratory pattern is characterized by a number of spectral parameters extracted from the respiratory and modulation frequency bands. The results show that the power of the modulation frequency band offers excellent performance when classifying CHF patients versus healthy subjects, with an accuracy of 95.3%, and nPB patients versus healthy subjects with 90.7%. The ratio between the power in the modulation and respiration frequency bands provides the best results classifying CHF patients into PB and nPB, with an accuracy of 88.9%.

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The process of weaning from mechanical ventilation is one of the challenges in intensive care. 149 patients under extubation process (T-tube test) were studied: 88 patients with successful trials (group S), 38 patients who failed to maintain spontaneous breathing and were reconnected (group F), and 23 patients with successful test but that had to be reintubated before 48 hours (group R). Each patient was characterized using 8 time series and 6 statistics extracted from respiratory and cardiac signals. A moving window statistical analysis was applied obtaining for each patient a sequence of patterns of 48 features. Applying a cluster analysis two groups with the majority dataset were obtained. Neural networks were applied to discriminate between patients from groups S, F and R. The best performance obtained was 84.0% of well classified patients using a linear perceptron trained with a feature selection procedure (that selected 19 of the 48 features) and taking as input the main cluster centroid. However, the classification baseline 69.8% could not be improved when using the original set of patterns instead of the centroids to classify the patients.

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Patients with chronic heart failure (CHF) with periodic breathing (PB) and Cheyne-Stokes respiration (CSR) tend to exhibit higher mortality and poor prognosis. This study proposes the characterization of respiratory patterns in CHF patients and healthy subjects using the envelope of the respiratory flow signal, and autoregressive (AR) time-frequency analysis. In time-varying respiratory patterns, the statistical distribution of the AR coefficients, pole locations, and the spectral parameters that characterize the discriminant band are evaluated to identify typical breathing patterns. In order to evaluate the accuracy of this characterization, a feature selection process followed by linear discriminant analysis is applied. 26 CHF patients (8 patients with PB pattern and 18 with non-periodic breathing pattern (nPB)) are studied. The results show an accuracy of 83.9% with the mean of the main pole magnitude and the mean of the total power, when classifying CHF patients versus healthy subjects, and 83.3% for nPB versus healthy subjects. The best result when classifying CHF patients into PB and nPB was an accuracy of 88.9%, using the coefficient of variation of the first AR coefficient and the mean of the total power.

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The Ischemic Heart Disease (IHD) is characterized by an insufficient supply with blood of the myocardium usually caused by an artherosclerotic disease of the coronary arteries (coronary artery disease CAD). The IHD and its consequences have become a leading problem in the industrialized nations. The aim of this study was to evaluate a new diagnosing method, the cardiogoniometry, using two different classifying techniques: the method of linear discriminant function analysis (LDA) and the method of Support Vector Machines (SVM). Data of a group of 109 female subjects (62 healthy, 47 with IHD) were analyzed on the basis of extracted parameters from the three-dimensional vector loops of the heart. The LDA achieved an accuracy of 83,5% (Sensitivity 78,7%, Specificity 87,1%), whereas the SVM achieved an accuracy of 86% (Sensitivity 80,5%, Specificity 89,8%). It could be shown that cardiogoniometry, an electrophysiological diagnostic method performed at rest, detects variables that are helpful in identifying ischemic heart disease. As it is easy to apply, non-invasive, and provides an automated interpretation it may become an inexpensive addition to the cardiologic diagnostic armamentarium, possibly useful for early diagnosis of IHD or CAD, as well as in patients who do not tolerate exercise testing. It was also proven that by applying Support Vector Machines an increased diagnostic precision in comparison to the conventional discriminant function analysis can be achieved.

JTD Keywords: Cardiogoniometry, Support Vector Machines, Nonlinear classifier, Linear discriminant analysis, Vector loop

Periodic breathing (PB) has a high prevalence in chronic heart failure (CHF) patients with mild to moderate symptoms and poor ventricular function. This work proposes the analysis and characterization of the respiratory pattern to identify periodic breathing pattern (PB) and non-periodic breathing pattern (nPB) through the respiratory flow signal. The respiratory pattern analysis is based on the extraction and the study of the flow envelope signal. The flow envelope signal is modelled by an autoregressive model (AR) whose coefficients would characterize the respiratory pattern of each group. The goodness of the characterization is evaluated through a linear and non linear classifier applied to the AR coefficients. An adaptive feature selection is used before the linear and non linear classification, employing leave-one-out cross validation technique. With linear classification the percentage of well classified patients (8 PB and 18 nPB patients) is 84.6% using the statistically significant coefficients whereas with non linear classification, the percentage of well classified patients increase to more than 92% applying the best subset of coefficients extracted by a forward selection algorithm.

JTD Keywords: Clinical-implications, Sleep

Mechanical ventilators are used to provide life support in patients with respiratory failure. Assessing autonomic control during the ventilator weaning provides information about physiopathological imbalances. Autonomic parameters can be derived and used to predict success in discontinuing from the mechanical support. Time-frequency analysis is used to derive cardiac and respiratory parameters, as well as their evolution in time, during ventilator weaning in 130 patients. Statistically significant differences have been observed in autonomic parameters between patients who are considered ready for spontaneous breathing and patients who are not. A classification based on respiratory frequency, heart rate and heart rate variability spectral components has been proposed and has been able to correctly classify more than 80% of the cases.

JTD Keywords: Automatic Data Processing, Databases, Factual, Electrocardiography, Humans, Models, Statistical, Respiration, Respiration, Artificial, Respiratory Insufficiency, Respiratory Mechanics, Respiratory Muscles, Signal Processing, Computer-Assisted, Time Factors, Ventilator Weaning, Ventilators, Mechanical, Work of Breathing