Manolis Maragoudakis
ABSTRACT
The large scale application of ICT-based assistive environment technologies for the home care of elderly and disabled people is going to generate huge numbers of signals transmitted from homes to local health centers or hospitals in order to be monitored by medical personnel. This task is going to be of critical importance and at the same time - if manually performed - quite demanding for specialized human resources and costly. In order to perform it in a cost-efficient manner it is necessary to develop mechanisms and methods for automated screening of these signals in order to identify abnormal ones that require some action to be taken. This paper proposes a method for automatic screening of heart sound signals, which are the most widely acquired signals from the human body for diagnostic purposes in both the 'traditional' medicine and the emerging ICT-based assistive environments. It is based on a novel Markov Chain Monte Carlo (MCMC) Bayesian Inference approach, which estimates conditional probability distributions in structures obtained from a Tree-Augmented Naïve Bayes (TAN) algorithm. The proposed approach has been applied and validated in a difficult heterogeneous dataset of 198 heart sound signals, which comes from both healthy medical cases and unhealthy ones having Aortic Stenosis, Mitral Regurgitation, Aortic Regurgitation or Mitral Stenosis. The proposed approach achieved a good performance in this difficult screening problem, which is higher than other widely used alternative classifiers, showing great potential for contributing to a cost-effective large scale application of ICT-based assistive environment technologies.
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Index Terms
- MCMC Bayesian inference for heart sounds screening in assistive environments
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