Identification of ECG Signal by Using Backpropagation Neural Network

Abstract

The heart rhythm abnormalities can be detected by interpreting the ECG signal. However, there are many ECG pattern to be interpreted. Neural network is an artificial intelligent based technique which can identify pattern and one of the most used learning algorithm is backpropagation. The system is built to help interpretation process of ECG signal in heart rhythm abnormalities diagnosis. The data used in this study are obtained from MIT-BIH database. The data has been pre-processed to be used in the developed identification system of this study. The pre-processing are separating signal into one cycle and equalize the size of the signal by using zero padded technique. In this system, a good feature extraction of the ECG signal is needed to increase the accuracy. Discrete wavelet transform is used as a feature extraction and the level which carrying out is until ten. Each level results are used as an input to the identification system and derive different accuracy value. For this study, the accuracy is set to 80%. The results below 80% considered not eligible. The developed identification system of ECG signal achieves an average 91.3% accuracy using ECG signal's feature calculated using Discrete Wavelet Transform technique in identifying normal and abnormal heart pulse.