Breathing Disorder Detection Using Wearable Electrocardiogram And Oxygen Saturation

Conventional diagnosis using polysomnography (PSG) on breathing disorder is expensive and uncomfortable to patients. In this paper, we present a low-cost portable and wearable multi-sensor system to non-invasively acquire a subject's vital signs, and leverage various machine learning methods on features extracted from Electrocardiogram (ECG) and Blood oxygen saturation (SpO2) signals to detect breathing disorder events. Our preliminary predication accuracies on 110 clinical patients is 90.0%.