Autonomic dysfunction increases cardiovascular risk in the presence of sleep apnea

The high prevalence of sleep apnea syndrome (SAS) and its direct relationship with an augmented risk of cardiovascular disease (CVD) have raised SAS as a primary public health problem. For this reason, extensive research aiming to understand the interaction between both conditions has been conducted. The advances in non-invasive autonomic nervous system (ANS) monitoring through heart rate variability (HRV) analysis have revealed an increased sympathetic dominance in subjects suffering from SAS when compared with controls. Similarly, HRV analysis of subjects with CVD suggests altered autonomic activity. In this work, we investigated the altered autonomic control in subjects suffering from SAS and CVD simultaneously when compared with SAS patients, as well as the possibility that ANS assessment may be useful for the early stage identification of cardiovascular risk in subjects with SAS. The analysis was performed over 199 subjects from two independent datasets during night-time, and the effects of the physiological response following an apneic episode, sleep stages, and respiration on HRV were taken into account. Results, as measured by HRV, suggest a decreased sympathetic dominance in those subjects suffering from both conditions, as well as in subjects with SAS that will develop CVDs, which was reflected in a significantly reduced sympathovagal balance (p < 0.05). In this way, ANS monitoring could contribute to improve screening and diagnosis, and eventually aid in the phenotyping of patients, as an altered response might have direct implications on cardiovascular health

Autonomic Dysfunction Increases Cardiovascular Risk in the Presence of Sleep Apnea

The high prevalence of sleep apnea syndrome (SAS) and its direct relationship with an augmented risk of cardiovascular disease (CVD) have raised SAS as a primary public health problem. For this reason, extensive research aiming to understand the interaction between both conditions has been conducted. The advances in non-invasive autonomic nervous system (ANS) monitoring through heart rate variability (HRV) analysis have revealed an increased sympathetic dominance in subjects suffering from SAS when compared with controls. Similarly, HRV analysis of subjects with CVD suggests altered autonomic activity. In this work, we investigated the altered autonomic control in subjects suffering from SAS and CVD simultaneously when compared with SAS patients, as well as the possibility that ANS assessment may be useful for the early stage identification of cardiovascular risk in subjects with SAS. The analysis was performed over 199 subjects from two independent datasets during night-time, and the effects of the physiological response following an apneic episode, sleep stages, and respiration on HRV were taken into account. Results, as measured by HRV, suggest a decreased sympathetic dominance in those subjects suffering from both conditions, as well as in subjects with SAS that will develop CVDs, which was reflected in a significantly reduced sympathovagal balance (p &lt; 0.05). In this way, ANS monitoring could contribute to improve screening and diagnosis, and eventually aid in the phenotyping of patients, as an altered response might have direct implications on cardiovascular health

A comparative study of ECG-derived respiration in ambulatory monitoring using the single-lead ECG

Cardiorespiratory monitoring is crucial for the diagnosis and management of multiple conditions such as stress and sleep disorders. Therefore, the development of ambulatory systems providing continuous, comfortable, and inexpensive means for monitoring represents an important research topic. Several techniques have been proposed in the literature to derive respiratory information from the ECG signal. Ten methods to compute single-lead ECG-derived respiration (EDR) were compared under multiple conditions, including different recording systems, baseline wander, normal and abnormal breathing patterns, changes in breathing rate, noise, and artifacts. Respiratory rates, wave morphology, and cardiorespiratory information were derived from the ECG and compared to those extracted from a reference respiratory signal. Three datasets were considered for analysis, involving a total 59 482 one-min, single-lead ECG segments recorded from 156 subjects. The results indicate that the methods based on QRS slopes outperform the other methods. This result is particularly interesting since simplicity is crucial for the development of ECG-based ambulatory systems

Multimodal Signal Analysis for Unobtrusive Characterization of Obstructive Sleep Apnea

Obstructive sleep apnea (OSA) is the most prevalent sleep related breathing disorder, nevertheless subjects suffering from it often remain undiagnosed due to the cumbersome diagnosis procedure. Moreover, the prevalence of OSA is increasing and a better phenotyping of patients is needed in order to prioritize treatment. The goal of this thesis was to tackle those challenges in OSA diagnosis, by means of advanced signal processing algorithms, proposed in this thesis. Additionally, two main algorithmic contributions, which are generally applicable were proposed. The binary interval coded scoring algorithm was extended to multilevel problems and novel monotonicity constraints were introduced. Moreover, improvements to the random-forest based feature selection were proposed including the use of the Cohen's kappa value, patient independent validation, and further feature pruning steered by the correlation between features. The first part of this thesis focused on the development of reliable, multimodal OSA screening methods based on unobtrusive measurements such as oxygen saturation (SpO2), electrocardiography (ECG), pulse photoplethysmography (PPG), and respiratory measures. The novel SpO2 model was the best performing OSA screening method, obtaining accuracies of over 88%, outperforming most of the state-of-the-art algorithms. Different multimodal OSA detection approaches were explored, but this performance could not be further improved. Finally, a main contribution of this PhD was to test the developed ECG and PPG OSA detection algorithms on unobtrusive signals, including capacitively-coupled ECG and bioimpedance, and wearable PPG recordings. Although these experiments showed promising results, the limitations of the current algorithms on the unobtrusive data were also highlighted. In the second part of this PhD a contribution towards a better characterization of OSA patients beyond the AHI was proposed. Novel pulse oximetry markers were developed and investigated to assess the cardiovascular status of OSA subjects. It was found that patients with cardiovascular comorbidities experienced more severe oxygen desaturations and incomplete resaturations to the baseline SpO2 values. The novel multilevel interval coded scoring was used to train a model to predict the cardiovascular status of OSA patients based on the age, BMI and the SpO2 parameters. The final model obtained good classification performances on a clinical population, but the predictive power of this model should be further validated.status: publishe

Are we training our heartbeat classification algorithms properly?

Despite the multiple studies dealing with heartbeat classification, the accurate detection of Supraventricular heartbeats (SVEB) is still very challenging. Therefore, this study aims to question the current protocol followed to report heartbeat classification results, which impedes the improvement of the SVEB class without falling on over-fitting. In this study, a novel approach based on Variational Mode Decomposition (VMD) as source of features is proposed, and the impact of the use of the MIT-BIH Arrhythmia database is analyzed.The method proposed is based on single-lead electrocardiogram, and it characterizes heartbeats by a set of 45 features: 5 related to the time intervals between consecutive heartbeats, and the rest related to VMD. Each heartbeat is decomposed in their variational modes, which are, on their turn, characterized by their frequency content, morphology and higher order statistics. The 10 most relevant features are selected using a backwards wrapper feature selector, and they are fed into an LS-SVM classifier, which is trained to separate Normal (N), Supraventricular (SVEB), Ventricular (VEB) and Fusion (F) heartbeats. An inter-patient approach, using patient independent training, is considered as suggested in the literature.The method achieves sensitivities above 80% for the three most important classes of the database (N, SVEB and VEB), and high specificities for the N and VEB classes. Given the challenges related to the SVEB and F class present in the literature, the composition of the MIT-BIH database is analyzed and alternatives are suggested in order to train heartbeat classification algorithms in a novel and more realistic way.status: publishe

Automatic screening of sleep apnea patients based on the sp02 signal

OBJECTIVE: This paper presents a methodology to automatically screen for sleep apnea based on the detection of apnea and hypopnea events in the blood oxygen saturation (SpO2) signal. METHODS: It starts by detecting all desaturations in the SpO2 signal. From these desaturations, a total of 143 time-domain features are extracted. After feature selection, the six most discriminative features are used to construct classifiers to predict if desaturations are caused by respiratory events. From these, a random forest classifier yielded the best classification performance. The number of desaturations, classified as caused by respiratory events per hour of recording, can then be used as an estimate of the apnea-hypopnea index (AHI), and to predict whether or not a patient suffers from sleep apnea-hypopnea syndrome (SAHS). All classifiers were developed based on a subset of 500 subjects of the Sleep Heart Health Study (SHHS) and tested on three different datasets, containing 8052 subjects in total. RESULTS: An averaged desaturation classification accuracy of 82.8% was achieved over the different test sets. Subjects having SAHS with an AHI greater than 15 can be detected with an average accuracy of 87.6%. CONCLUSION: The achieved SAHS screening outperforms SpO2 methods from the literature on the SHHS test dataset. Moreover, the robustness of the method was shown when tested on different independent test sets. SIGNIFICANCE: These results show that an algorithm based on simple features of SpO2 desaturations can outperform more elaborate methods in the detection of apneic events and the screening of SAHS patients.status: publishe

Feature Selection Algorithm based on Random Forest applied to Sleep Apnea Detection

This paper presents a new feature selection method based on the changes in out-of-bag (OOB) Cohen kappa values of a random forest (RF) classifier, which was tested on the automatic detection of sleep apnea based on the oxygen saturation signal (SpO2). The feature selection method is based on the RF predictor importance defined as the increase in error when features are permuted. This method is improved by changing the classification error into the Cohen kappa value, by adding an extra factor to avoid correlated features and by adapting the OOB sample selection to obtain a patient independent validation. When applying the method for sleep apnea classification, an optimal feature set of 3 parameters was selected out of 286. This was half of the 6 features that were obtained in our previous study. This feature reduction resulted in an improved interpretability of our model, but also a slight decrease in performance, without affecting the clinical screening performance. Feature selection is an important issue in machine learning and especially biomedical informatics. This new feature selection method introduces interesting improvements of RF feature selection methods, which can lead to a reduced feature set and an improved classifier interpretability.status: publishe

Sleep Apnea Hypopnea Syndrome Classification in SpO2 Signals using Wavelet Decomposition and Phase Space Reconstruction

© 2017 IEEE. Sleep Apnea Hypopnea Syndrome (SAHS) is a sleep disorder where patients experience multiple airflow cessations or reductions during the night. It is recognized as a common condition with a population prevalence of 1% to 6.5%. The Apnea Hypopnea Index (AHI) characterizes the severity of SAHS using signals obtained from Polysomnography (PSG); this requires the use of multiple sensors on the patient and an overnight hospital stay. The development of cheaper and more comfortable screening techniques involving wearable devices is, therefore, desirable. This paper presents a method based on wavelet decomposition and phase space reconstruction with embedding dimensions for feature extraction from oxygen saturation measured in SpO2 signals. The proposed characteristics are the areas spanned by each wavelet level in the phase space calculated using the convex hull algorithm. These areas are then fed into a classifier that groups the patients in categories of AHI higher or lower than 5. The results show an accuracy of 93% using K-Nearest Neighbors (Knn), and 88.61% using Least Square Support Vector Machines (LS-SVM).status: publishe

Detection and Classification of Sleep Apnea and Hypopnea Using PPG and SpO2 Signals

International audienceIn this work, a detection and classification method for sleep apnea and hypopnea, using photopletysmography (PPG) and peripheral oxygen saturation (2) signals, is proposed. The detector consists of two parts: one that detects reductions in amplitude fluctuation of PPG (DAP)and one that detects oxygen desaturations. To further differentiate among sleep disordered breathing events (SDBE), the pulse rate variability (PRV) was extracted from the PPG signal, and then used to extract features that enhance the sympatho-vagal arousals during apneas and hypopneas. A classification was performed to discriminate between central and obstructive events, apneas and hypopneas. The algorithms were tested on 96 overnight signals recorded at the UZ Leuven hospital, annotated by clinical experts, and from patients without any kind of co-morbidity. An accuracy of 75.1% for the detection of apneas and hypopneas, in one-minute segments,was reached. The classification of the detected events showed 92.6% accuracy in separating central from obstructive apnea, 83.7% for central apnea and central hypopnea and 82.7% for obstructive apnea and obstructive hypopnea. The low implementation cost showed a potential for the proposed method of being used as screening device, in ambulatory scenarios

Pulse Photoplethysmography derived respiration for obstructive sleep apnea detection

© 2017 IEEE Computer Society. All rights reserved. Five time series which are known to be modulated by respiration are derived from the pulse photoplethysmographic (PPG) signal, and they are analyzed for obstructive sleep apnea (OSA) detection: Pulse rate, amplitude, and width variabilities (PRV, PAV, and PWV, respectively), pulse upslopes, and slope transit time (STT). A total of 26 polysomnographic recordings were split in 1-min segments which were manually labeled as OSA (653 segments), normal breathing (7204 segments), or other pulmonary events. For each one of the 5 PPG-derived series, 4 features were extracted: the standard deviation, the power at high and low frequency (PLF) bands, and the normalized PLF. These 20 features were used as input of a least-squares support vector machine classifier using an RBF kernel. Results show an accuracy of 72.66%, suggesting that the analyzed features are promising for the detection of OSA from only the PPG signal.status: publishe