An automatic rules extraction approach to support OSA events detection in a mHealth system

Detection and real-time monitoring of Obstructive Sleep Apnea (OSA) episodes are very important tasks in healthcare. To suitably face them, this paper proposes an easy-touse, cheap mobile-based approach relying on three steps. Firstly, single-channel ECG data from a patient are collected by a wearable sensor and are recorded on a mobile device. Secondly, the automatic extraction of knowledge about that patient takes place offline, and a set of IF...THEN rules containing Heart Rate Variability (HRV) parameters is achieved. Thirdly, these rules are used in our real-time mobile monitoring system: the same wearable sensor collects the single-channel ECG data and sends them to the same mobile device, which now processes those data online to compute HRV-related parameter values. If these values activate one of the rules found for that patient, an alarm is immediately produced. This approach has been tested on a literature database with thirty-five OSA patients. A comparison against five well-known classifiers has been carried out.