Capacitively-coupled ECG and respiration for the unobtrusive detection of sleep apnea.

OBJECTIVE: The performance of a novel unobtrusive system based on capacitively-coupled electrocardiography (ccECG) combined with different respiratory measurements is evaluated for the detection of sleep apnea. APPROACH: A sleep apnea detection algorithm is proposed, which can be applied to electrocardiography (ECG) and ccECG, combined with different unobtrusive respiratory measurements, including ECG derived respiration (EDR), respiratory effort measured using the thoracic belt (TB) and capacitively-coupled bioimpedance (ccBioz). Several ECG, respiratory and cardiorespiratory features were defined, of which the most relevant ones were identified using a random forest based backwards wrapper. Using this relevant feature set, a least-squares support vector machine classifier was trained to decide if a one minute segment is apneic or not, based on the annotated polysomnography (PSG) data of 218 patients suspected of having sleep apnea. The obtained classifier was then tested on the PSG and capacitively-coupled data of 28 different patients. MAIN RESULTS: On the PSG data, an AUC of 76.3% was obtained when the ECG was combined with the EDR. Replacing the EDR with the TB led to an AUC of 80.0%. Using the ccECG and ccBioz or the ccECG and TB resulted in similar performances as on the PSG data, while using the ccECG and ccECG-based EDR resulted in a drop in AUC to 67.4%. SIGNIFICANCE: This is the first study which tests an apnea detection algorithm on capacitively-coupled ECG and bioimpedance signals and shows promising results on the capacitively-coupled data set. However, it was shown that the EDR could not be accurately estimated from the ccECG signals. Further research into the effect that respiration has on the ccECG is needed to propose alternative EDR estimates.

Detection and Classification of Sleep Apnea and Hypopnea Using PPG and SpO 2 Signals.

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

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.

Multilevel Interval Coded Scoring to Assess the Cardiovascular Status of Sleep Apnea Patients Using Oxygen Saturation Markers.

OBJECTIVE: Studies have shown an increased cardiovascular risk in obstructive sleep apnea (OSA) patients. In order to prioritize treatment of high risk patients, there is a need for improved cardiovascular OSA phenotyping. This study investigates the use of oxygen saturation (SpO 2) parameters for cardiovascular risk assessment of OSA patients. To this end, a novel multilevel interval coded scoring (mICS) algorithm is proposed. METHODS: The study includes SpO 2 recordings from 1987 overnight polysomnographies, of which 974 are from patients suspected to have OSA, 931 from the general population based Sleep Heart Health Study and 83 from healthy controls. The minimal SpO 2 value, SpO 2 upslope and amplitude ratio of desaturation over resaturation are extracted for all oxygen desaturations and averaged per patient. These three SpO 2 parameters are used together with patient demographics to develop a mICS model to predict the probability that a patient had a cardiovascular condition, or had already experienced a cardiovascular event, at the time of the polysomnography. RESULTS: Including the SpO 2 parameters in the mICS together with age and BMI improves the model's performance by 2.7% and leads to a test area under the curve (AUC) of 69.5% for the detection of any cardiovascular comorbidity. Moreover, an increase in AUC of 5% was obtained for the detection of cardiovascular events, resulting in an AUC of 93.5%. CONCLUSIONS: This study shows that parameters based on SpO 2 and the mICS model are useful to predict the cardiovascular comorbidity status of OSA patients. SIGNIFICANCE: The proposed model could be used to assist in prioritizing OSA patients for treatment.

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.

Evaluation of Methods to Characterize the Change of the Respiratory Sinus Arrhythmia with Age in Sleep Apnea Patients.

The High Frequency (HF) band of the power spectrum of the Heart Rate Variability (HRV) is widely accepted to contain information related to the respiration. However, it is known that this often results in misleading estimations of the strength of the Respiratory Sinus Arrhythmia (RSA). In this paper, different approaches to characterize the change of the RSA with age, combining HRV and respiratory signals, are studied. These approaches are the bandwidths in the power spectral density estimations, bivariate phase rectified signal averaging, information dynamics, a time-frequency representation, and a heart rate decomposition based on subspace projections. They were applied to a dataset of sleep apnea patients, specifically to periods without apneas and during NREM sleep. Each estimate reflected a different relationship between RSA and age, suggesting that they all capture the cardiorespiratory information in a different way. The comparison of the estimates indicates that the approaches based on the extraction of respiratory information from HRV provide a better characterization of the age-dependent degradation of the RSA.

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.

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.

Automatic Screening of Sleep Apnea Patients Based on the SpO2 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.