Sleep stage and obstructive apneaic epoch classification using single-lead ECG

Abstract

BackgroundPolysomnography (PSG) is used to define physiological sleep and different physiological sleep stages, to assess sleep quality and diagnose many types of sleep disorders such as obstructive sleep apnea. However, PSG requires not only the connection of various sensors and electrodes to the subject but also spending the night in a bed that is different from the subject's own bed. This study is designed to investigate the feasibility of automatic classification of sleep stages and obstructive apneaic epochs using only the features derived from a single-lead electrocardiography (ECG) signal.MethodsFor this purpose, PSG recordings (ECG included) were obtained during the night's sleep (mean duration 7 hours) of 17 subjects (5 men) with ages between 26 and 67. Based on these recordings, sleep experts performed sleep scoring for each subject. This study consisted of the following steps: (1) Visual inspection of ECG data corresponding to each 30-second epoch, and selection of epochs with relatively clean signals, (2) beat-to-beat interval (RR interval) computation using an R-peak detection algorithm, (3) feature extraction from RR interval values, and (4) classification of sleep stages (or obstructive apneaic periods) using one-versus-rest approach. The features used in the study were the median value, the difference between the 75 and 25 percentile values, and mean absolute deviations of the RR intervals computed for each epoch. The k-nearest-neighbor (kNN), quadratic discriminant analysis (QDA), and support vector machines (SVM) methods were used as the classification tools. In the testing procedure 10-fold cross-validation was employed.ResultsQDA and SVM performed similarly well and significantly better than kNN for both sleep stage and apneaic epoch classification studies. The classification accuracy rates were between 80 and 90% for the stages other than non-rapid-eye-movement stage 2. The accuracies were 60 or 70% for that specific stage. In five obstructive sleep apnea (OSA) patients, the accurate apneaic epoch detection rates were over 89% for QDA and SVM.ConclusionThis study, in general, showed that RR-interval based classification, which requires only single-lead ECG, is feasible for sleep stage and apneaic epoch determination and can pave the road for a simple automatic classification system suitable for home-use.

Identification of sleep apnea events using discrete wavelet transform of respiration, ECG and accelerometer signals

“…This result has, to the authors knowledge, never previously been highlighted. Many authors have discussed the sole use of an ECG signal to classify the data [5][8] [24] however results shown within this paper instead propose the use of a simple accelerometer signal for classification purposes. However more accurate results are available if features from the ECG data is also included in the analysis.…”

“…Current research is continuing to examine the use of less complex systems to accurately classify sleep apnea events. Examples of this research includes the use of the ECG to classify between obstructive and central apnea events [8] [24] and the use of accelerometers placed on the suprasternal notch to screen for sleep apnea events [13].…”

Analysis of Efficient Biometric Index using Heart Rate Variability for Remote Monitoring of Obstructive Sleep Apnea

“…To analyze OSA events on basis of AHI and RDI following conditions to be fulfilled: healthy subject (AHI/RDI) < 5; mild (5 ≤ AHI/RDI ≤ 15, moderate (15<AHI/RDI ≤ 30), and severe (AHI/RDI>30) [31][32][33]. In this study, Polysomnography (PSG) method is used as a reference to acquiring indexes such as AHI and RDI to signify the occurrence of OSA events.…”

Sleep apnea detection using time-delayed heart rate variability

“…A pool of eleven existent HRV features in the time domain (known in literature [12] [19]) was extracted for SA detection. As mentioned, this work focused on showing the applicability of the time-delayed scheme rather than achieving best detection results.…”