Title :
Detecting Obstructive Sleep Apnea events in a real-time mobile monitoring system through automatically extracted sets of rules
Author :
Sannino, Giovanna ; De Falco, Ivanoe ; De Pietro, G.
Author_Institution :
Inst. of High-Performance Comput. & Networking, Naples, Italy
Abstract :
Performing detection and real-time monitoring of Obstructive Sleep Apnea (OSA) is a significant healthcare task. An easy, cheap, and mobile approach to monitor patients with OSA is proposed here. It gathers data from a patient by a single-channel ECG, and offline automatically extracts knowledge about that patient as a set of IF...THEN rules containing Heart Rate Variability (HRV) parameters. These rules are then used in the real-time mobile monitoring system: ECG data is collected by a wearable sensor, sent to a mobile device, and processed online to compute HRV-related parameter values. If a rule is activated by those values, the system produces an alarm. A literature database of OSA patients has been used to test the approach.
Keywords :
body sensor networks; electrocardiography; health care; knowledge acquisition; medical disorders; medical signal detection; mobile computing; patient monitoring; real-time systems; sleep; ECG data; HRV-related parameter values; Heart Rate Variability parameters; IF...THEN rules; OSA patients; healthcare task; literature database; mobile device; obstructive sleep apnea event detection; offline automatic knowledge extraction; real-time mobile monitoring system; rule sets; single-channel ECG; wearable sensor; Biomedical monitoring; Databases; Electrocardiography; Mobile communication; Monitoring; Real-time systems; Sleep apnea; IF…THEN rules; Knowledge extraction; Obstructive Sleep Apnea; Real-time monitoring system; Wearable sensors;
Conference_Titel :
e-Health Networking, Applications & Services (Healthcom), 2013 IEEE 15th International Conference on
Conference_Location :
Lisbon
Print_ISBN :
978-1-4673-5800-2
DOI :
10.1109/HealthCom.2013.6720630
