SAMON: Sleep apnea monitoring

Patients suspected of suffering Sleep Apnea and Hypopnea Syndrome (SAHS) have to undergo sleep studies such as expensive polysomnographies to be diagnosed. Healthcare professionals are constantly looking for ways to improve the ease of diagnosis and comfort for this kind of patients as well as reducing both the number of sleep studies they need to undergo and the waiting times. In this paper we present an alternative proposal that promotes not only a transmission of physiological data but also a real-time analysis of these data locally at a mobile device. For that, we have built a classifier that provides an accuracy of 93% and a ROC-AUC of 98.5% on SpO₂ signals available in the annotated Apnea-ECG Database.