Sleep Apnea Detection using Features from the Respiration and the ECG recorded with Smart-Shirts

The automatic detection of sleep apnea episodes, without the need of polysomnography and outside a clinical facility, could help facilitate the diagnosis of this disorder. In this work, features to detect sleep apnea events were computed from respiration and electrocardiogram recordings acquired with a wearable smart-shirt. First, a classical scheme exploiting the amplitude decrease of the respiration during apnea episodes was presented. Second, a novel measure of the phase coupling between the respiration and the respiratory sinus arrhythmia from the ECG was introduced. It was shown that these features were significantly different during sleep apnea episodes than for normal breathing.