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A novel method combining a genetic algorithm and a back propagation neural network (GA-BPNN) with wavelet packet decomposition achieves over 97% accuracy and reduces feature dimensions by nearly 50% in the automatic classification of ECG signals. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

January 31, 2017Scientific Reports196 citationsOpen Access

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

HLHongqiang Li DYDanyang Yuan XMXiangdong Ma

Structured PICO

Population

360 ECG signal samples from the MIT-BIH arrhythmia database (6 types: N, L, R, P, V, A) and 300 samples from an experimental platform (FLUKE ProSim 2 vital sign simulator)

Intervention

Genetic algorithm-back propagation neural network (GA-BPNN) with wavelet packet decomposition (WPD) for feature extraction

Comparator

Single BPNN classifier, SVM, GA-SVM, and other literature methods

Outcome

Classification accuracy of ECG signals

The proposed GA-BPNN method with WPD feature extraction achieves high accuracy in classifying ECG arrhythmias, demonstrating potential for automated cardiac diagnosis.

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.

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