Remote Health Monitoring of heart failure with data mining via CART method on HRV features

Pecchia, Leandro and Melillo, Paolo and Marcello, Bracale (2011) Remote Health Monitoring of heart failure with data mining via CART method on HRV features. IEEE Transactions on Biomedical Engineering, 58 (3). pp. 800-804. ISSN 0018-9294

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Official URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5638128

Abstract

Disease Management Programs (DMPs), which use no advanced ICT, are as effective as telemedicine but more efficient because less costly. We proposed a platform to enhance effectiveness and efficiency of home monitoring using data mining for early detection of any worsening in patient’s condition. These worsening could require more complex and expensive care if not recognized. In this paper, we briefly describe the Remote Health Monitoring (RHM) platform we designed and realized, which supports Heart Failure (HF) severity assessment offering functions of data mining based on Classification and Regression Tree (CART) method. The system developed achieved accuracy and a precision respectively of 96.39% and 100.00% in detecting HF and of 79.31% and 82.35% in distinguish severe versus mild HF. These preliminary results were achieved on public databases of signals to improve their reproducibility. Clinical trials involving local patients are still running and will require longer experimentation.

Item Type:Article
Additional Information:Copyright © IEEE 2011. Self-archiving by authors on their own personal servers or the servers of their institutions or employers is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending an email to pubs-permissions@ieee.org.
Uncontrolled Keywords:Home Monitoring, HRV, Heart Failure, worsening early detection, CHF, diseases management program, data mining, CART, telemedicine.
Schools/Departments:Faculty of Engineering
ID Code:1580
Deposited By:Pecchia, Dr Leandro
Deposited On:13 Feb 2012 10:09
Last Modified:13 Feb 2012 10:09

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