title: An automatic rules extraction approach to support OSA events detection in an mHealth system.
abstract: 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.
authors.forename: Giovanna
authors.initials: G
authors.lastname: Sannino
authors.forename: Ivanoe
authors.initials: I
authors.lastname: De Falco
authors.forename: Giuseppe
authors.initials: G
authors.lastname: De Pietro
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted; D004562:Electrocardiography; D006339:Heart Rate; D006801:Humans; D017286:Polysomnography; D012815:Signal Processing, Computer-Assisted; D020181:Sleep Apnea, Obstructive / Q000175:diagnosis*; D017216:Telemedicine / Q000379:methods*