An optimized convolutional neural network for arrhythmia classification - Shan, Wei Chen (2022)
Electrocardiogram (ECG) is a practical medical test to diagnose arrhythmia. As a crucial computational application in clinical practice, ECG automatic classification can effectively detect the possible occurrence of cardiovascular disease. At present, the main problems in the automated classification of ECG are due to (1) the complexity of algorithms to capture heartbeats, (2) the complex changes of irregular heartbeats in rhythm or morphology leading to difficulties in the ECG feature recognition, and (3) the needs of large training samples and training time for a machine learning to achieve the ideal recognition accuracy. Given the problems in ECG automatic classification, this study proposes an effective automated classification approach for arrhythmia based on a representative convolution neural network that can decode ECG source files and identify heartbeats accurately based on the detection of QRS waveform from the ECG records. A one-dimensional convolutional neural network (1D-CNN) is proposed to accurately classify different types of arrhythmias by automatically extracting the morphological features of ECG. The initial connection weights of 1D-CNN are optimized based on differential evolution to improve its ECG classification. The optimized 1D-CNN is evaluated against two arrhythmia databases, namely the MITBIH and SCDH arrhythmia databases. Besides, a comparison is made between the optimized and unoptimized 1D-CNN. The results show that the proposed model has higher accuracy in heartbeat classification. Compared to the unoptimized 1D-CNN, the accuracy improves by 0.6% and 3.1%, respectively. Besides, the optimized 1D-CNN requires less training time, 9.22 seconds less with MIT-BIH and 10.35 seconds less with SCDH based on ReLU active function and 10 epochs, as compared to the unoptimized 1D-CNN based on the same parameter settings. The training time of the optimized 1DCNN decreased by 67.2% and 64.2% with MIT-BIH and SCDH, respectively.