NDLI: The heart disease diagnosing system based on force sensitive chair's measurement, biorthogonal wavelets and neural networks

Content Provider	IEEE Xplore Digital Library
Author	Akhbardeh, A. Junnila, S. Koivuluoma, M. Koivistoinen, T. Varri, A.
Copyright Year	2005
Description	Author affiliation: Inst. of Signal Process., Tampere Univ. of Technol. (Akhbardeh, A.; Junnila, S.; Koivuluoma, M.)
Abstract	The heart disease diagnosing (HDD) system consists of a sensitive movement EMFI-film sensor installed under the upholstery of a chair. Whilst a man rests on the chair, this force sensitive sensor produces a single electrical signal containing components reflective of cardiac-ballistocardiogram (BCG), respiratory, and body movement related motion. Among different measurements of body activities, BCG has an interesting property that no electrodes are needed to be attached to the body during recording. This makes it suitable for evaluation of a man's heart condition in any place such as home, car, or office. This paper describes briefly our developed HDD system and especially a combined intelligent signal processing method to detect, extract features, and finally cluster BCG cycles. The system is designed to assist medical doctors to diagnose heart diseases of subject under test. Indeed, it is a fully automatic system which is not sensitive to any BCG latency as well as non-linear disturbance. It uses high resolution biorthogonal wavelet transform to extract essential BCG features and then clusters them using artificial neural networks (ANNs). Some evaluations using recordings from normal young, normal old and abnormal old volunteers indicated that our combined method is reliable and has a high performance
Starting Page	676
Ending Page	681
File Size	502630
Page Count	6
File Format	PDF
ISBN	0780390474
DOI	10.1109/AIM.2005.1511060
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-07-24
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Force measurement Cardiac disease Neural networks Force sensors Biosensors Feature extraction Sensor systems Electrodes Heart Biomedical signal processing
Content Type	Text
Resource Type	Article

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