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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.

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

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.

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

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.

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

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.

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

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.

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

El Síndrome de Apnea-Hipopnea del Sueño (SAHS) actualmente se diagnostica mediante la Polisomnografía (PSG), una prueba cara y costosa. Se han propuesto diversas alternativas para ayudar al cribado previo de SAHS. En estudios previos demostramos que los sujetos con SAHS se pueden identificar a partir de las frecuencias de resonancia (formantes) de la respiración nocturna. En este trabajo se extiende el estudio al sonido respiratorio registrado en vigilia. Se seleccionaron diversos ciclos de inspiración y expiración consecutivas en 23 sujetos con diversos grados de SAHS durante el estado de vigilia previo a la PSG. Mediante un modelo autoregresivo (AR) se estimaron los formantes y el área transversal (CSA) de la vía. Se observa que los formantes en determinadas bandas tienen una frecuencia mayor (p<0.04) en sujetos con SAHS levemoderado, con un Índice de Apnea-Hipopnea (AHI) menor que 30, respecto a los sujetos con SAHS severo (AHI≥30). En paralelo, el área promedio de la vía aérea en las zonas con obstrucción muestra una tendencia decreciente (r=-0.498) con la severidad de la patología. Las características de los formantes, combinadas con medidas antropométricas, permiten clasificar a los sujetos con SAHS severo con una sensibilidad (especificidad) de hasta un 84.6% (88.9%). En conclusión, el sonido respiratorio registrado durante vigilia proporciona información valiosa sobre el estado de la vía aérea superior que puede ayudar a identificar un SAHS severo.

Obstructive Sleep Apnea (OSA) is currently diagnosed by a full nocturnal polysomnography (PSG), a very expensive and time-consuming method. In previous studies we were able to distinguish patients with OSA through formant frequencies of breath sound during sleep. In this study we aimed at identifying OSA patients from breath sound analysis during wakefulness. The respiratory sound was acquired by a tracheal microphone simultaneously to PSG recordings. We selected several cycles of consecutive inspiration and exhalation episodes in 10 mild-moderate (AHI<;30) and 13 severe (AHI>=30) OSA patients during their wake state before getting asleep. Each episode's formant frequencies were estimated by linear predictive coding. We studied several formant features, as well as their variability, in consecutive inspiration and exhalation episodes. In most subjects formant frequencies were similar during inspiration and exhalation. Formant features in some specific frequency band were significantly different in mild OSA as compared to severe OSA patients, and showed a decreasing correlation with OSA severity. These formant characteristics, in combination with some anthropometric measures, allowed the classification of OSA subjects between mild-moderate and severe groups with sensitivity (specificity) up to 88.9% (84.6%) and accuracy up to 86.4%. In conclusion, the information provided by formant frequencies of tracheal breath sound recorded during wakefulness may allow identifying subjects with severe OSA.

Keywords: Correlation, Databases, Sensitivity, Sleep apnea, Speech, Synchronization

El Síndrome de Apnea-Hipopnea del Sueño (SAHS) tiene una incidencia en sujetos de edad media, del 2-4% en mujeres y 4- 6% en hombres, además de múltiples consecuencias asociadas. Sin embargo, a pesar de su prevalencia, menos de un 10% de la población con este síndrome es diagnosticada. Con el objetivo de identificar qué señales debería emplear un futuro método de diagnóstico para pacientes con sospecha de SAHS más eficaz que los actuales, se sugiere un estudio en detalle de los eventos respiratorios que tienen lugar durante la noche. Para ello se parte de los estudios de monitorización del sueño realizados a pacientes con síntomas de SAHS mediante dos plataformas comerciales distintas. En primer lugar, los registros procedentes de dichos estudios se combinan y sincronizan temporalmente de una forma precisa y robusta. Una vez llevada y sincronizada toda la información a una plataforma común, el presente estudio se centra en la relación del SAHS con una nueva información, el roncograma. El concograma permite estudiar la evolución de los ronquidos según la fase de sueño. Aplicando esta medida sobre nuestra base de datos observamos como el tiempo en fase de vigilia, el tiempo en fase REM o la densidad de ronquidos en fases ligeras presentan diferencias estadísticamente significativas para pacientes con distinta severidad de SAHS.

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Sleep apnea-hypopnea syndrome (SAHS) is a serious sleep disorder, and snoring is one of its earliest and most consistent symptoms. We propose a new methodology for identifying two distinct types of snores: the so-called non-regular and regular snores. Respiratory sound signals from 34 subjects with different ranges of Apnea-Hypopnea Index (AHI = 3.7-109.9 h -1) were acquired. A total number of 74,439 snores were examined. The time interval between regular snores in short segments of the all night recordings was analyzed. Severe SAHS subjects show a shorter time interval between regular snores (p = 0.0036, AHI cp: 30 h -1) and less dispersion on the time interval features during all sleep. Conversely, lower intra-segment variability (p = 0.006, AHI cp: 30 h -1) is seen for less severe SAHS subjects. Features derived from the analysis of time interval between regular snores achieved classification accuracies of 88.2 % (with 90 % sensitivity, 75 % specificity) and 94.1 % (with 94.4 % sensitivity, 93.8 % specificity) for AHI cut-points of severity of 5 and 30 h -1, respectively. The features proved to be reliable predictors of the subjects' SAHS severity. Our proposed method, the analysis of time interval between snores, provides promising results and puts forward a valuable aid for the early screening of subjects suspected of having SAHS.

Keywords: Sleep apnea, Snore sounds, Snore time interval

The gold standard for diagnosing sleep apnoea-hypopnoea syndrome (SAHS) is polysomnography (PSG), an expensive, labour-intensive and time-consuming procedure. Accordingly, it would be very useful to have a screening method to allow early assessment of the severity of a subject, prior to his/her referral for PSG. Several differences have been reported between simple snorers and SAHS patients in the acoustic characteristics of snoring and its variability. In this paper, snores are fully characterised in the time domain, by their sound intensity and pitch, and in the frequency domain, by their formant frequencies and several shape and energy ratio measurements. We show that accurate multiclass classification of snoring subjects, with three levels of SAHS, can be achieved on the basis of acoustic analysis of snoring alone, without any requiring information on the duration or the number of apnoeas. Several classification methods are examined. The best of the approaches assessed is a Bayes model using a kernel density estimation method, although good results can also be obtained by a suitable combination of two binary logistic regression models. Multiclass snore-based classification allows early stratification of subjects according to their severity. This could be the basis of a single channel, snore-based screening procedure for SAHS.

Keywords: Bayes classifier, Kernel density estimation, Sleep apnoea, Snoring

El ronquido es un sonido respiratorio que se produce durante el sueño, ya sea nocturno o diurno. El ronquido puede ser inspiratorio, espiratorio o puede ocupar todo el ciclo respiratorio. Tiene su origen en la vibración de los diferentes tejidos de la vía aérea superior. Se han descrito numerosos métodos para analizarlo, desde el simple interrogatorio, pasando por cuestionarios estándares, hasta llegar a los métodos acústicos más sofisticados, que se han desarrollado gracias al gran avance de las técnicas biomédicas en los últimos años. El presente trabajo describe el estado del arte actual en los procedimientos de análisis del ronquido.

Keywords: Ronquido, Apnea del sueño, Síndrome de apnea-hipoapnea del sueño, Snoring, Sleep apnea, Sleep Apnea and Hipoapnea Syndrome

The gold standard for diagnosing Sleep Apnea Hypopnea Syndrome (SAHS) is the Polysomnography (PSG), an expensive, labor-intensive and time-consuming procedure. It would be helpful to have a simple screening method that allowed to early determining the severity of a subject prior to his/her enrolment for a PSG. Several differences have been reported in the acoustic snoring characteristics between simple snorers and SAHS patients. Previous studies usually classify snoring subjects into two groups given a threshold of Apnea-Hypoapnea Index (AHI). Recently, Bayes multi-group classification with Gaussian Probability Density Function (PDF) has been proposed, using snore features in combination with apnea-related information. In this work we show that the Bayes classifier with Kernel PDF estimation outperforms the Gaussian approach and allows the classification of SAHS subjects according to their severity, using only the information obtained from snores. This could be the base of a single

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Snoring is one of the earliest and most consistent sign of upper airway obstruction leading to Sleep Apnea-Hypopnea Syndrome (SAHS). Several studies on post-apneic snores, snores that are emitted immediately after an apnea, have already proven that this type of snoring is most distinct from that of normal snoring. However, post-apneic snores are more unlikely and sometimes even inexistent in simple snorers and mild SAHS subjects. In this work we address that issue by proposing the study of normal non-regular snores. They correspond to successive snores that are separated by normal breathing cycles. The results obtained establish the feasibility of acoustic parameters of normal non-regular snores as a promising tool for a prompt screening of SAHS severity.

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Several studies have shown differences in acoustic snoring characteristics between patients with Sleep Apnea-Hypopnea Syndrome (SAHS) and simple snorers. Usually a few manually isolated snores are analyzed, with an emphasis on postapneic snores in SAHS patients. Automatic analysis of snores can provide objective information over a longer period of sleep. Although some snore detection methods have recently been proposed, they have not yet been applied to full-night analysis devices for screening purposes. We used a new automatic snoring detection and analysis system to monitor snoring during full-night studies to assess whether the acoustic characteristics of snores differ in relation to the Apnea-Hypopnea Index (AHI) and to classify snoring subjects according to their AHI. A complete procedure for device development was designed, using databases with polysomnography (PSG) and snoring signals. This included annotation of many types of episodes by an expert physician: snores, inspiration and exhalation breath sounds, speech and noise artifacts, The AHI of each subject was estimated with classical PSG analysis, as a gold standard. The system was able to correctly classify 77% of subjects in 4 severity levels, based on snoring analysis and sound-based apnea detection. The sensitivity and specificity of the system, to identify healthy subjects from pathologic patients (mild to severe SAHS), were 83% and 100%, respectively. Besides, the Apnea Index (AI) obtained with the system correlated with the obtained by PSG or Respiratory Polygraphy (RP) (r=0.87, p<0.05).

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Objectives/Hypothesis: We used a new automatic snoring detection and analysis system to monitor snoring during full-night polysomnography to assess whether the acoustic characteristics of snores differ in relation to the apnea-hypopnea index (AHI) and to classify subjects according to their AHI Study Design: Individual Case-Control Study. Methods: Thirty-seven snorers (12 females and 25 males, ages 40-65 years; body mass index (BMI), 29.65 +/- 4.7 kg/m(2)) participated Subjects were divided into three groups: G1 (AHI <5), G2 (AHI >= 5, <15) and G3 (AHI >= 15) Snore and breathing sounds were : recorded with a tracheal microphone throughout 6 hours of nighttime polysomnography The snoring episodes identified were automatically and continuously analyzed with a previously trained 2-layer feed-forward neural network. Snore number, average intensity, and power spectral density parameters were computed for every subject and compared among AHI groups. Subjects were classified using different AHI thresholds by means of a logistic regression model. Results: There were significant differences in supine position between G1 and G3 in sound intensity, number of snores; standard deviation of the spectrum, power ratio in bands 0-500, 100-500, and 0-800 Hz, and the symmetry coefficient (P < .03); Patients were classified with thresholds AHI = 5 and AHI = 15 with a sensitivity (specificity) of 87% (71%) and 80% (90%), respectively. Conclusions: A new system for automatic monitoring and analysis of snores during the night is presented. Sound intensity and several snore frequency parameters allow differentiation of snorers according to obstructive sleep apnea syndrome severity (OSAS). Automatic snore intensity and frequency monitoring and analysis could be a promising tool for screening OSAS patients, significantly improving the managing of this pathology.

Keywords: Breathing sounds, Signal interpretation, Sleep apnea syndromes, Snoring

Sleep Apnea-Hypopnea Syndrome (SAHS) diagnosis is still done with an overnight multi-channel polysomnography. Several efforts are being made to study profoundly the snore mechanism and discover how it can provide an opportunity to diagnose the disease. This work introduces the concept of regular snores, defined as the ones produced in consecutive respiratory cycles, since they are produced in a regular way, without interruptions. We applied 2 thresholds (TH/sub adaptive/ and TH/sub median/) to the time interval between successive snores of 34 subjects in order to select regular snores from the whole all-night snore sequence. Afterwards, we studied the effectiveness that parameters, such as time interval between successive snores and the mean intensity of snores, have on distinguishing between different levels of SAHS severity (AHI (Apnea-Hypopnea Index)<5h/sup -1/, AHI<10 h/sup -1/, AHI<15h/sup -1/, AHI<30h/sup -1/). Results showed that TH/sub adaptive/ outperformed TH/sub median/ on selecting regular snores. Moreover, the outcome achieved with non-regular snores intensity features suggests that these carry key information on SAHS severity.

Keywords: Practical, Experimental/ acoustic signal processing, Bioacoustics, Biomedical measurement, Diseases, Feature extraction, Medical signal processing, Patient diagnosis, Pneumodynamics, Sleep/ nonregular snore features, SAHS markers, Sleep apnea hypopnea syndrome, Overnight multichannel polysomnography, Snore mechanism

Several differences between the airway of normal subjects and those with OSAS are well known. The characteristics of the upper airway may be indirectly studied through the formant frequencies of breathing sounds. In this work we analyze the formants of inspiration and exhalation sounds in snoring subjects with and without OSAS. Formant frequencies of inspiration and exhalation appear in the same bands as snores. Formant F1 is significantly lower in inspiration episodes of OSAS patients (p=0.008) with a decreasing tendency as the AHI increases (r=0.705). In addition, this formant has a significantly higher variability SF1 in pathological subjects, for both inspiration (p=0.022) and exhalation (p=0.038) episodes, as was previously found in snores. A higher variability of formant frequencies seems to be an indicator of the presence of OSAS. The proposed technique could allow the identification of OSAS patients from normal breathing alone.

Keywords: Upper airway