Convolutional neural network optimized by differential evolution for electrocardiogram classification

Shan Wei Chen; Shir Li Wang; XiuZhi Qi; Theam Foo Ng; Haidi Ibrahim

doi:10.1007/s11042-023-15407-9

Convolutional neural network optimized by differential evolution for electrocardiogram classification

Multimed Tools Appl. 2023 Apr 26:1-27. doi: 10.1007/s11042-023-15407-9. Online ahead of print.

Authors

Shan Wei Chen^{1

2}, Shir Li Wang^{1

3}, XiuZhi Qi⁴, Theam Foo Ng⁵, Haidi Ibrahim⁶

Affiliations

¹ Faculty of Computing and Meta-Technology, Universiti Pendidikan Sultan Idris, 35900 Tanjong Malim, Perak Malaysia.
² Department of Education, Baoji University of Arts and Sciences, Baoji, 721012 China.
³ Data Intelligent and Knowledge Management (DILIGENT), Universiti Pendidikan Sultan Idris, 35900 Tanjong Malim, Perak Malaysia.
⁴ Department of Arts, Baoji University of Arts and Sciences, Baoji, 721012 China.
⁵ Centre for Global Sustainability Studies, Universiti Sains Malaysia, 11800 Minden, Penang Malaysia.
⁶ School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Engineering Campus, 14300 Nibong Tebal, Penang Malaysia.

Abstract

The Coronavirus disease 2019, or COVID-19, has shifted the medical paradigm from face-to-face to telehealth. Telehealth has become a vital resource to contain the virus spread and ensure the continued care of patients. In terms of preventing cardiovascular diseases, automating electrocardiogram (ECG) classification is a promising telehealth intervention. The healthcare service ensures that patient care is appropriate, comfortable, and accessible. Convolutional neural networks (CNNs) have demonstrated promising results in ECG categorization, which require high accuracy and short training time to ensure healthcare quality. This study proposes a one-dimensional-CNN (1D-CNN) arrhythmia classification based on the differential evolution (DE) algorithm to optimize the accuracy of ECG classification and training time. The performance of 1D-CNNs of different activation functions are optimized based on the standard DE algorithm. Finally, based on MIT-BIH and SCDH arrhythmia databases, the performances of optimized and unoptimized 1D-CNN are compared and analysed. Results show that the 1D-CNN optimized by the DE has higher accuracy in heartbeats classification. The optimized 1D-CNN improves from 97.6% to 99.5% on MIT-BIH and from 80.2% to 88.5% on SCDH. Therefore, the optimized 1D-CNN shows improvements of 1.9% and 8.3% in the two datasets, respectively. In addition, compared with the unoptimized 1D-CNN based on the same parameter settings, the optimized 1D-CNN has less training time. Under the conditions of ReLU function and 10 epochs, the training takes 9.22 s on MIT-BIH and 10.35 s on SCDH, reducing training time by 67.2% and 64.2%, respectively.

Keywords: Convolutional neural network; Differential evolution; Electrocardiogram classification.

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