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

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.

An improved genetic algorithm for designing optimal temporal patterns of neural stimulation

“…GA options now represent a parameter set so large that some groups have turned to applying one search algorithm to optimize another (Jing et al 2010) and have found success, even in the field of neuroscience (Li et al 2017). Our proposed modifications can also be combined with other GA improvements or variations that modify other elements of the algorithm.…”

Artificial Neural Network Classification of Motor-Related EEG: An Increase in Classification Accuracy by Reducing Signal Complexity

“…In this context, the best performance has been obtained for SVM with nonlinear kernel based on radial basis function (RBF-SVM). It was shown that while the use of linear SVM with spatial and temporal principal component analysis (PCA) demonstrated 73% accuracy [40], RBF-SVM allowed to reach up to 93% accuracy in combination with independent component analysis (ICA) [41] and 81% in combination with genetic algorithm (GA) [42,43]. The radial basis function (RBF) neural network architecture was applied by Barios et al [44] to classify patients with chronic renal failure and demonstrated 86.6% accuracy without optimization.…”

“…Genetic algorithms based on ANNs are effectively used for feature optimization of biological signals, such as electroencephalography (EEG) and electrocorticography (ECoG). For instance, a genetic algorithm was used by Li et al [43] for optimization of the input channels combination for the MLP-based neural network and relevance evaluation of each EEG channel to a current task. It was revealed that the channel selection provides better understanding of results obtained by the classifier.…”

Wearable ECG signal processing for automated cardiac arrhythmia classification using CFASE‐based feature selection

“…Substantial methods for automated classification of cardiac arrhythmia have been proposed in recent years. These methods for classifying cardiac arrhythmia use a variety of features, including time-domain features, frequency-domain features, time-frequency domain features, and morphological features (Bogovski, 2012;Huang, Liu, Zhu, Wang, & Hu, 2014;Lin & Yang, 2014;Jatmiko, Nulad, Matul, Setiawan, & Mursanto, 2011;Ince, Kiranyaz, & Gabbouj, 2009;Übeyli, 2007;Giri et al, 2013;Jayachandran, Joseph, & Acharya, 2010;Li, Yuan, Ma, Cui, & Cao, 2017;Qin, Li, Zhang, Yue, & Liu, 2017). Bogovski et al presented a method that used time-domain features and support vector machine (SVM) to classify five types of ECG heartbeats (Bogovski, 2012).…”

“…A method based on DWT and entropy was used to classify two types of ECG heartbeats by Jayachandran et al (Jayachandran et al, 2010). Genetic algorithm (GA) and the back propagation neural network (BPNN) were employed to classify six types of ECG heartbeats by Li H et al (Li et al, 2017). Q. Qin et al used low-dimensional wavelet features and SVM for six types of ECG heartbeats classification (Qin et al, 2017).…”