Publications

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

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

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

The study of the mechanomyographic signal of the diaphragm muscle (MMGdi) is a promising technique in order to evaluate the respiratory muscles effort. The relationship between amplitude and frequency parameters of this signal with the respiratory effort performed during respiration is of great interest for researchers and physicians due to its diagnostic potentials. However, MMGdi signals are frequently contaminated by a cardiac vibration or mechanocardiographic (MCG) signal. An adaptive noise cancellation (ANC) can be used to reduce the MCG interference in the recorded MMGdi activity. In this paper, it is evaluated the proposed ANC scheme by means of a synthetic MMGdi signal with a controlled MCG interference. The Pearson's correlation coefficient (PCC) between both root mean square (RMS) and mean frequency (fm) of the synthetic MMGdi signal are considerably reduced with the presence of cardiac vibration noise (from 0.95 to 0.87, and from 0.97 to 0.76, respectively). With the ANC algorithm proposed the effect of the MCG noise on the amplitude and frequency of MMG parameters is reduced considerably (PCC of 0.93 and 0.97 for the RMS and fm, respectively). The ANC method proposed in this work is an interesting technique to attenuate the cardiac interference in respiratory MMG signals. Further investigation should be carried out to evaluate the performance of the ANC algorithm in real MMGdi signals.

JTD Keywords: Adaptive filters, Frequency modulation, Interference, Muscles, Noise cancellation, Vibrations, Cardiology, Medical signal processing, Muscle, Signal denoising, ANC algorithm, MCG interference, Pearson correlation coefficient, Adaptive noise cancellation, Cardiac vibration interference, Cardiac vibration noise, Diaphragm muscle, Mechanocardiographic signal, Mechanomyographic signals, Respiratory muscles effort

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