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

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 of apnea and hypopnea events occurring. We show that the proposed method can mitigate the effect of irregular breathing that occurs after apnea and hypopnea events. The performance was validated using overnight recordings from seven patients, showing a 1.4-fold improvement in apnea and hypopnea event detection compared with the non-overlapping method.