Automatic Home-based Screening of Obstructive Sleep Apnea Using Single Channel Electrocardiogram and SPO2 Signals

TL;DR

This paper presents a novel, online, home-based method for detecting obstructive sleep apnea using single-channel ECG and SpO2 signals, achieving over 85% accuracy across multiple datasets.

Contribution

It introduces a combined feature selection and machine learning approach for real-time OSA detection using minimal biological signals, improving practicality and performance.

Findings

Achieved over 85% detection accuracy across three databases.

Used mutual information for optimal feature selection.

Implemented multiple classifiers for robust detection.

Abstract

Obstructive sleep apnea (OSA) is one of the most widespread respiratory diseases today. Complete or relative breathing cessations due to upper airway subsidence during sleep is OSA. It has confirmed potential influence on Covid-19 hospitalization and mortality, and is strongly associated with major comorbidities of severe Covid-19 infection. Un-diagnosed OSA may also lead to a variety of severe physical and mental side-effects. To score OSA severity, nocturnal sleep monitoring is performed under defined protocols and standards called polysomnography (PSG). This method is time-consuming, expensive, and requiring professional sleep technicians. Automatic home-based detection of OSA is welcome and in great demand. It is a fast and effective way for referring OSA suspects to sleep clinics for further monitoring. On-line OSA detection also can be a part of a closed-loop automatic control of the OSA therapeutic/assistive devices. In this paper, several solutions for online OSA detection are introduced and tested on 155 subjects of three different databases. The best combinational solution uses mutual information (MI) analysis for selecting out of ECG and SpO2-based features. Several methods of supervised and unsupervised machine learning are employed to detect apnoeic episodes. To achieve the best performance, the most successful classifiers in four different ternary combination methods are used. The proposed configurations exploit limited use of biological signals, have online working scheme, and exhibit uniform and acceptable performance (over 85%) in all the employed databases. The benefits have not been gathered all together in the previous published methods.