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 (SpO) signal. It starts by detecting all desaturations in the SpO 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 if a patient suffers from sleep apnea-hypopnea syndrome (SAHS) or not. 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. An averaged desaturation classification accuracy of 82.8 % was achieved over the different test sets. Subjects having SAHS with an AHI larger than 15 can be detected with an average accuracy of 87.6 %. The achieved SAHS screening outperforms SpO methods from the literature on the SHHS test dataset. Moreover, the robustness of the method was shown when tested on different independent test sets. These results show that an algorithm based on simple features of SpO desaturations can outperform more elaborate methods in the detection of apneic events and the screening of SAHS patients.

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 onemin, 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. Continuous monitoring of respiration plays a key role in the detection and management of different conditions, such as stress 1,2 and sleep disorders 3,4. Biomarkers like respiratory rate, breathing phases, and tidal volume are relevant for the detection of mental stress 1 , anxiety 2 , and sleep apnea events 5,6. In addition, the coupling between respiration and heart rate has been used as a biomarker for the aforementioned conditions 7,8 as well as for the understanding of the interactions between the cardiac and respiratory systems 9. Despite the importance of monitoring respiration, its recording requires the use of invasive and intrusive sensors like thermistors, spirometers, and respiratory belts. Even though these sensors are regularly used, for instance during polysomnographic recordings, their use in ambulatory systems is very limited since they not only interfere with natural breathing, but are often associated with high costs and low comfort. Different studies have shown that the respiratory rate, and even the respiratory wave morphology, can be approximated by ECG-derived respiration (EDR) 5,10-20. The derived signal is defined by certain morphological properties of the ECG particularly influenced by respiration. This influence can be explained by the respiratory-induced chest movements that cause changes in the position of the electrodes relative to the cardiac vector 21. Moreover, the filling and emptying of the lungs cause changes in the electrical impedance of the chest. As a result, the morphology of the ECG is modulated by respiration.

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.

In this work, a detection and classification method for sleep apnea and hypopnea, using photopletysmography (PPG) and peripheral oxygen saturation (SpO2) 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.

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This study investigates the use of pulse photoplethysmography (PPG) for the detection of sleep apnea and its added value to oxygen saturation (SpO2) based detection. PPG-time series known to be modulated by both respiration and the autonomous nervous system were derived: pulse rate, amplitude and width variability, slope transit time, maximal pulse upslope and the area under the PPG peak. Moreover, the instantaneous power in the high and low frequency band of the pulse rate was estimated using a point-process model. For all extracted time series, five features were computed over a 1 minute interval: the mean, minimum and maximum value, standard deviation and gradient. Feature selection resulted in the 6 most discriminative features for PPG based detection of apneic minutes. These features were used as input for a least-squares support vector machine classifier, which was applied on polysomnographic data of 102 subjects suspected of having sleep apnea-hypopnea syndrome. A classification accuracy of 68.7 % was achieved. When SpO2 features were added to the classifier the accuracy increased to 83.4 %, which is only slightly higher than the 82.2 % obtained using only SpO2. These results show the potential of PPG features for sleep apnea detection, however, their added value to SpO2 is limited.

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 factorto 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,

Acquisition of capacitively-coupled ECG (ccECG) from daily life scenarios is limited by its sensitivity to motion and its variability in signal quality. 48 features, in combination with different classifiers, were evaluated for quality classification on a dataset of 10000 ccECG segments of 15 seconds. Feature subsets with potential high discriminatory power were identified and evaluated in multiple supervised models, for two classification problems with different tolerance to artefacts. This resulted in balanced accuracies of 94.02% and 92.4%, achieved using a Linear SVM and a fine KNN respectively. These models are useful tools for real-time and offline processing of ccECG signals recorded in real-life scenarios