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.
@article{arxiv.2505.19701,
title = {Accurate Radar-Based Detection of Sleep Apnea Using Overlapping Time-Interval Averaging},
author = {Kodai Hasegawa and Shigeaki Okumura and Hirofumi Taki and Hironobu Sunadome and Satoshi Hamada and Susumu Sato and Kazuo Chin and Takuya Sakamoto},
journal= {arXiv preprint arXiv:2505.19701},
year = {2025}
}
Comments
5 pages, 3 figures, and 2 tables. This work is going to be submitted to the IEEE for possible publication