Automatic extraction of effective rule sets for Obstructive Sleep Apnea detection for a real-time mobile monitoring system

Real-time Obstructive Sleep Apnea (OSA) detection and monitoring are important for the society in terms of improvement in citizens' health conditions and of reduction in mortality and healthcare costs. This paper proposes an easy, cheap, and portable approach for monitoring patients with OSA. It is based on singlechannel ECG data, and on the automatic offline extraction, from a database containing ECG information about the monitored patient, of explicit knowledge under the form of a set of IF...THEN rules containing typical parameters derived from Heart Rate Variability (HRV) analysis. This set of rules can be exploited in our realtime mobile monitoring system: ECG data is gathered by a wearable sensor and sent to a mobile device, where it is processed in real time, HRV-related parameters are computed from it, and, if their values activate some of the rules describing occurrence of OSA, an alarm is automatically produced. The approach has been tested on a well-known literature database of OSA patients. Rules are obtained which are specific for each patient. Numerical results have shown the effectiveness of the approach, and the achieved sets of rules evidence its user-friendliness. Furthermore, the method has been compared against other well-known classifiers.