Genetic algorithm for the optimization of features and neural networks in ECG signals classification

Hongqiang Li,Dianyin Cui,Lu Cao,Xiangdong Ma,Danyang Yuan

doi:10.1038/srep41011

Abstract

Feature extraction and classification of electrocardiogram (ECG) signals are necessary for the automatic diagnosis of cardiac diseases. In this study, a novel method based on genetic algorithm-back propagation neural network (GA-BPNN) for classifying ECG signals with feature extraction using wavelet packet decomposition (WPD) is proposed. WPD combined with the statistical method is utilized to extract the effective features of ECG signals. The statistical features of the wavelet packet coefficients are calculated as the feature sets. GA is employed to decrease the dimensions of the feature sets and to optimize the weights and biases of the back propagation neural network (BPNN). Thereafter, the optimized BPNN classifier is applied to classify six types of ECG signals. In addition, an experimental platform is constructed for ECG signal acquisition to supply the ECG data for verifying the effectiveness of the proposed method. The GA-BPNN method with the MIT-BIH arrhythmia database achieved a dimension reduction of nearly 50% and produced good classification results with an accuracy of 97.78%. The experimental results based on the established acquisition platform indicated that the GA-BPNN method achieved a high classification accuracy of 99.33% and could be efficiently applied in the automatic identification of cardiac arrhythmias.

Highlights

Feature extraction and classification of electrocardiogram (ECG) signals are necessary for the automatic diagnosis of cardiac diseases
Many studies have developed arrhythmia recognition approaches that utilize automatic analysis and diagnosis systems based on ECG signals[4,5,6,7], in which feature extraction and classification are important for the analysis and diagnosis of cardiac diseases
Discrete biorthogonal wavelet decomposition was utilized for extracting ECG features, and a radial basis function neural network was used for ECG classification[11]

Summary

Introduction

Feature extraction and classification of electrocardiogram (ECG) signals are necessary for the automatic diagnosis of cardiac diseases. Thereafter, the optimized BPNN classifier is applied to classify six types of ECG signals. The experimental results based on the established acquisition platform indicated that the GA-BPNN method achieved a high classification accuracy of 99.33% and could be efficiently applied in the automatic identification of cardiac arrhythmias.

Results

Conclusion