Heart sound screening in real-time assistive environments through MCMC Bayesian data mining

Abstract

Emerging pervasive assistive environment applications for remote home healthcare monitoring of the elderly, disabled and also patients with various chronic diseases generate massive amounts of sensor signal data, which are transmitted from numerous homes to local health centers or hospitals. While it is critical to process this data efficiently (in a fast and accurate manner) and cost-effectively, in a large-scale application of the above technologies, it is not possible to do so manually by specialized human resources. This paper proposes a methodology for automatic real-time screening of heart sound signals (one of the most widely acquired signals from the human body for diagnostic purposes) and identification of those that are abnormal and require some action to be taken, which can be applied to many other similar types of bio-signals generated in assistive environments. It is based on a novel Markov Chain Monte Carlo Bayesian Inference approach, which estimates conditional probability distributions in structures obtained from a Tree-Augmented Naive Bayes algorithm. It has been applied and validated in a highly `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 methodology achieved high classification performance in this difficult screening problem. It performs higher than other widely used classifiers, showing great potential for contributing to a cost-effective large-scale application of ICT-based assistive environment technologies.