Accurate Radar-Based Detection of Sleep Apnea Using Overlapping Time-Interval Averaging

TL;DR

This paper introduces a radar-based method for sleep apnea detection that uses overlapping time-interval averaging to improve accuracy, especially after irregular breathing episodes, validated with patient data.

Contribution

It presents a novel overlapping interval averaging technique to enhance radar-based sleep apnea detection accuracy over previous methods.

Findings

Mitigates irregular breathing effects post-apnea events.

Improves detection accuracy with overlapping interval averaging.

Validated with experimental data from seven patients.

Abstract

Radar-based respiratory measurement is a promising tool for the noncontact detection of sleep apnea. Our team has reported that apnea events can be accurately detected using the statistical characteristics of the amplitude of respiratory displacement. However, apnea and hypopnea events are often followed by irregular breathing, reducing the detection accuracy. This study proposes a new method to overcome this performance degradation by repeatedly applying the detection method to radar data sets corresponding to multiple overlapping time intervals. Averaging the detected classes over multiple time intervals gives an analog value between 0 and 1, which can be interpreted as the probability that there is an apnea event. We show that the proposed method can mitigate the effect of irregular breathing that occurs after apnea / hypopnea events, and its performance is confirmed by experimental data taken from seven patients.