http://www.cnr.it/ontology/cnr/individuo/prodotto/ID282408
An automatic rules extraction approach to support OSA events detection in a mHealth system (Articolo in rivista)
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- An automatic rules extraction approach to support OSA events detection in a mHealth system (Articolo in rivista) (literal)
- Anno
- 2014-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1109/JBHI.2014.2311325 (literal)
- Alternative label
Giovanna Sannino, Ivanoe De Falco, Giuseppe De Pietro (2014)
An automatic rules extraction approach to support OSA events detection in a mHealth system
in IEEE Journal of Biomedical and Health Informatics; IEEE, New York (Stati Uniti d'America)
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- Giovanna Sannino, Ivanoe De Falco, Giuseppe De Pietro (literal)
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- Titolo
- An automatic rules extraction approach to support OSA events detection in a mHealth system (literal)
- 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-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. (literal)
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