An Automatic Rules Extraction Approach to Support OSA Events Detection in an mHealth System

作者：Sannino Giovanna^*; De Falco Ivanoe; De Pietro Giuseppe

来源：IEEE Journal of Biomedical and Health Informatics, 2014, 18(5): 1518-1524.

DOI：10.1109/JBHI.2014.2311325

• IF...then rules
• knowledge extraction
• obstructive sleep apnea (OSA)
• real-time monitoring system
• wearable sensors

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-to-use, cheap mobile-based approach relying on three steps. First, single-channel ECG data from a patient are collected by a wearable sensor and are recorded on a mobile device. Second, 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. Third, 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 35 OSA patients. A comparison against five well-known classifiers has been carried out.

• 出版日期2014-9