Noncontact Detection of Sleep Apnea Using Radar and Expectation-Maximization Algorithm

TL;DR

This paper introduces a radar-based, noncontact method utilizing the expectation-maximization algorithm for accurate sleep apnea detection, aiming to improve patient comfort and diagnostic precision.

Contribution

It presents a novel radar and EM algorithm-based approach for adaptive sleep apnea detection without empirical parameters, validated through clinical experiments.

Findings

Detects apnea/hypopnea events with an error of 4.8 times/hour

Improves detection accuracy by 1.8 times over conventional methods

Demonstrates effectiveness in clinical patient measurements

Abstract

Sleep apnea syndrome requires early diagnosis because this syndrome can lead to a variety of health problems. If sleep apnea events can be detected in a noncontact manner using radar, we can then avoid the discomfort caused by the contact-type sensors that are used in conventional polysomnography. This study proposes a novel radar-based method for accurate detection of sleep apnea events. The proposed method uses the expectation-maximization algorithm to extract the respiratory features that form normal and abnormal breathing patterns, resulting in an adaptive apnea detection capability without any requirement for empirical parameters. We conducted an experimental quantitative evaluation of the proposed method by performing polysomnography and radar measurements simultaneously in five patients with the symptoms of sleep apnea syndrome. Through these experiments, we show that the proposed method can detect the number of apnea and hypopnea events per hour with an error of 4.8 times/hour; this represents an improvement in the accuracy by 1.8 times when compared with the conventional threshold-based method and demonstrates the effectiveness of our proposed method.