An obstructive sleep apnea detection approach using kernel density classification based on single-lead electrocardiogram

Journal of Medical Systems
Lili ChenHui Wang

Abstract

Obstructive sleep apnea (OSA) is a common sleep disorder that often remains undiagnosed, leading to an increased risk of developing cardiovascular diseases. Polysomnogram (PSG) is currently used as a golden standard for screening OSA. However, because it is time consuming, expensive and causes discomfort, alternative techniques based on a reduced set of physiological signals are proposed to solve this problem. This study proposes a convenient non-parametric kernel density-based approach for detection of OSA using single-lead electrocardiogram (ECG) recordings. Selected physiologically interpretable features are extracted from segmented RR intervals, which are obtained from ECG signals. These features are fed into the kernel density classifier to detect apnea event and bandwidths for density of each class (normal or apnea) are automatically chosen through an iterative bandwidth selection algorithm. To validate the proposed approach, RR intervals are extracted from ECG signals of 35 subjects obtained from a sleep apnea database ( http://physionet.org/cgi-bin/atm/ATM ). The results indicate that the kernel density classifier, with two features for apnea event detection, achieves a mean accuracy of 82.07 %, with mean sensitivity of...Continue Reading

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Citations

Feb 29, 2020·European Annals of Otorhinolaryngology, Head and Neck Diseases·M AkkariM Mondain
May 1, 2021·Biomedizinische Technik. Biomedical Engineering·Sajjad AfrakhtehFatemeh Soltani
May 18, 2021·International Journal of Cardiology·Jin-Yu SunXiang-Qing Kong

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