A New Self-Supervised Learning Method for Sleep Apnea Detection from Radar Signals

Obstructive Sleep Apnea and Hypopnea (OSAH) is the most prevalent sleep-related breathing disorder, which has a significant impact on human health. Currently, polysomnography (PSG) devices, as the gold standard for diagnosing this disease, have several drawbacks, including high costs, discomfort during use, and unsuitability for home monitoring. In contrast, radar devices offer a non-contact and convenient monitoring approach, with significant potential for application. However, manual annotation of nighttime radar data is often time-consuming and labor-intensive, which makes the apnea detection task even more challenging. To address this, we propose a deep learning method based on contrastive self-supervised learning for detecting OSAH from radar signals. We evaluated the proposed method using a public dataset with different training-test splits. The experimental results demonstrate that our method outperforms existing state-of-the-art methods, achieving the highest classification accuracy of 98.97%. Notably, even with only 10% labeled data, the method still achieved an accuracy of 86.00%. These results indicate that the proposed method effectively enables monitoring of OSAH from radar signals, while significantly reducing the workload of manual annotation.