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

Abstract

The heart disease diagnosing (HDD) system consists of a sensitive movement EMFi™-film¹ 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.

Introduction

The heart disease diagnosing (HDD) system uses a force sensitive electromechanical film (EMFi™) sensor installed under the upholstery of a chair. The sensor generates a movement related signal consisting of components attributable to the ballistocardiogram (BCG), respiration, and body movement. EMFi™ sensor film is an elastic electret material consisting of three distinct layers: two smooth and homogeneous surface layers, and a thicker mid section full of flat air voids separated by leaf-like polypropylene layers. External force supplied to the film surface changes the thickness of the air voids and causes charges residing on the polypropylene/void interfaces to move with respect to each other. As a result, a charge proportional to the force (pressure) applied to the film is generated to the film electrodes (Lekkala and Paajanen, 1999). BCG which typically will trail the ECG by about 0.1–0.3 s is an interesting Bio-measurement for diagnosing, monitoring and managing myocardial disorders related to heart diseases. The disorders are characterized by sudden recurrent and transient disturbances of myocardial function and/or mechanical movements of the heart. The presence of any abnormal patterns in BCG points towards an abnormal heart condition. However, sometimes for normal subjects, in special situations, there are some similar patterns compared to abnormal heart disease related BCG patterns. During the past several years, a large number of biomedical signal processing methods have been developed, including single and multi channel template matching, principle component analysis, amplitude separation, Fourier analysis, linear filtering, autoregressive modeling, neural networks, and maximum likelihood. In the field of BCG signal processing also some methods have been developed and some of them are listed in Yu et al. (1996). Most of the existing methods perform very well while the problem of Motion artifacts, and BCG waveform's latency as well as non-linear disturbances such as electrical drifting of electronic devices or another noise sources are not considered. However, methods that do not deal with such an important issue may potentially give us untrue information about the patient. Other limitations of the existing techniques concern their degree of success in the cases of special situations of subject such as stress, their ease of hardware and/or software implementation, their portability across platforms, and their suitability for real-time processing. To overcome these problems, we used some high-resolution methods in our developed systems which are explained in next sections.