Towards a heart disease diagnosing system based on force sensitive chair's measurement, biorthogonal wavelets and neural networks

  • Authors:

  • Alireza Akhbardeh;Sakari Junnila;Mikko Koivuluoma;Teemu Koivistoinen;Vainö Turjanmaa;Tiit Kööbi;Alpo Värri

  • Affiliations:

  • Institute of Signal Processing, Tampere University of Technology, PO Box 553, 33101 Tampere, Finland;Institute of Signal Processing, Tampere University of Technology, PO Box 553, 33101 Tampere, Finland;Institute of Signal Processing, Tampere University of Technology, PO Box 553, 33101 Tampere, Finland;Department of Clinical Physiology, Tampere University Hospital, PO Box 2000, 33521 Tampere, Finland;Department of Clinical Physiology, Tampere University Hospital, PO Box 2000, 33521 Tampere, Finland;Department of Clinical Physiology, Tampere University Hospital, PO Box 2000, 33521 Tampere, Finland;Institute of Signal Processing, Tampere University of Technology, PO Box 553, 33101 Tampere, Finland

  • Venue:
  • Engineering Applications of Artificial Intelligence
  • Year:
  • 2007

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Abstract

The heart disease diagnosing (HDD) system consists of a sensitive movement EMFi(TM)-film sensor installed under the upholstery of a chair. Whilst a man rests on the chair, this sensor which is sensitive to force gives us a single electrical signal containing components reflective of cardiac-ballistocardiogram (BCG), respiratory, and body movements related motion. Among different measurements of body activities, BCG has the interesting property that no electrodes are needed to be attached to the body during recording, suitable to evaluate man heart condition in any place such as home, car, or his 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 for assisting medical doctors to diagnose heart diseases of person under test. Indeed, it is a fully automatic system which is not very sensitive to the BCG latency as well as non-linear disturbance. It uses high resolution Biorthogonal wavelet transforms to extract essential BCG features and to cluster those 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 high performance.