Sleep Respiration Monitoring Using Attention-reinforced Radar Signals

Existing contactless solutions on sleep respiration monitoring are either performed in controlled environments, having poor usability in practical scenarios, or only provide coarse-grained respiration rates, being unable to accurately detect abnormal events of patients. In this paper, we propose Respnea, a non-invasive sleep respiration monitoring system using an impulse-radio ultra-wideband (IR-UWB) radar. Particularly, we propose a profiling algorithm, which can locate the sleep positions in non-controlled environments and identify different states of subjects. Further, we construct a deep learning model which adopts a multi-headed self-attention and learn the patterns implicit in the respiration signal so as to distinguish sleep respiration events at a granularity of seconds. We conduct experiments on data collected from patients with sleep disorders and healthy subjects. The experimental results show that Respnea achieves a low error (less than 0.27 bpm) in respiration rate estimation and reaches the accuracy of 88.89% diagnosing the severity of Sleep Apnea-Hypopnea Syndrome.