Classification of Body Movements in Ambulatory ECG Using Wavelet Transform, Adaptive Filter and Artificial Neural Networks

Sachin Darji

doi:10.4172/2157-7420.1000170

Abstract

Ambulatory ECG (A-ECG) monitoring provides electrical activity of the heart when a person is involved in doing normal routine activities. Thus, the recorded ECG signal consists of cardiac signal along with motion artifacts introduced due to person’s body movements during routine activities. Detection of motion artifacts due to different physical activities might help in further cardiac diagnosis. Ambulatory ECG signal analysis for detection of various body movements using Discrete Wavelet Transform (DWT) and adaptive filtering approaches has been addressed in this paper. The ECG signals of five healthy subjects (aged between 22 to 30 years) were recorded while the person performs various body movements like up and down movement of left hand, up and down movement of right hand, waist twisting movement while standing and change from sitting down on chair to standing up movement in lead I configuration using BIOPAC MP 36 data acquisition system. The features of motion artifact signal, extracted using Gabor transform, have been fed to the train the artificial neural network (ANN) for classifying body movements.

Highlights

Ambulatory ECG signal monitoring is useful when long term cardiac monitoring of a person is necessary
The major challenge with ambulatory ECG monitoring is that the cardiac signal gets contaminated due to motion artifacts resulting due to body movements [1]
It is observed that the artificial neural network (ANN) classification performance based on motion artifacts extracted using Discrete Wavelet Transform (DWT) approach results in 93.70% accuracy as compared to 89.07% obtained by adaptive filtering approach

Summary

Introduction

Ambulatory ECG signal monitoring is useful when long term cardiac monitoring of a person is necessary. The major challenge with ambulatory ECG monitoring is that the cardiac signal gets contaminated due to motion artifacts resulting due to body movements [1]. Namely the discrete wavelet transform (DWT) and adaptive filtering, have been applied on the acquired A-ECG signal to extract the motion artifacts. The coefficients selection procedure was repeated for all the A-ECG signals involving described body movement activities in a similar manner discussed above.

Results

Conclusion