Arrhythmia classification based on multi-feature multi-path parallel deep convolutional neural networks and improved focal loss

Zhongnan Ran; Mingfeng Jiang; Yang Li; Zhefeng Wang; Yongquan Wu; Wei Ke; Ling Xia; Zhongnan Ran; Mingfeng Jiang; Yang Li; Zhefeng Wang; Yongquan Wu; Wei Ke; Ling Xia

doi:10.3934/mbe.2024243

Mathematical Biosciences and Engineering

2024, Volume 21, Issue 4: 5521-5535. doi: 10.3934/mbe.2024243

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Research article

Arrhythmia classification based on multi-feature multi-path parallel deep convolutional neural networks and improved focal loss

1.
School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China
2.
School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China
3.
Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, No. 2 Anzhen Road, Chaoyang District, Beijing 100029, China
4.
School of Applied Sciences, Macao Polytechnic Institute, Macao SAR, China
5.
Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China

Academic Editor: Giovanna Guidoboni

Received: 22 December 2023 Revised: 08 February 2024 Accepted: 07 March 2024 Published: 22 March 2024

Early diagnosis of abnormal electrocardiogram (ECG) signals can provide useful information for the prevention and detection of arrhythmia diseases. Due to the similarities in Normal beat (N) and Supraventricular Premature Beat (S) categories and imbalance of ECG categories, arrhythmia classification cannot achieve satisfactory classification results under the inter-patient assessment paradigm. In this paper, a multi-path parallel deep convolutional neural network was proposed for arrhythmia classification. Furthermore, a global average RR interval was introduced to address the issue of similarities between N vs. S categories, and a weighted loss function was developed to solve the imbalance problem using the dynamically adjusted weights based on the proportion of each class in the input batch. The MIT-BIH arrhythmia dataset was used to validate the classification performances of the proposed method. Experimental results under the intra-patient evaluation paradigm and inter-patient evaluation paradigm showed that the proposed method could achieve better classification results than other methods. Among them, the accuracy, average sensitivity, average precision, and average specificity under the intra-patient paradigm were 98.73%, 94.89%, 89.38%, and 98.24%, respectively. The accuracy, average sensitivity, average precision, and average specificity under the inter-patient paradigm were 91.22%, 89.91%, 68.23%, and 95.23%, respectively.
- arrhythmia classification,
- ECG signal,
- deep convolution neural network,
- multi-path parallel
Citation: Zhongnan Ran, Mingfeng Jiang, Yang Li, Zhefeng Wang, Yongquan Wu, Wei Ke, Ling Xia. Arrhythmia classification based on multi-feature multi-path parallel deep convolutional neural networks and improved focal loss[J]. Mathematical Biosciences and Engineering, 2024, 21(4): 5521-5535. doi: 10.3934/mbe.2024243

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Abstract

Early diagnosis of abnormal electrocardiogram (ECG) signals can provide useful information for the prevention and detection of arrhythmia diseases. Due to the similarities in Normal beat (N) and Supraventricular Premature Beat (S) categories and imbalance of ECG categories, arrhythmia classification cannot achieve satisfactory classification results under the inter-patient assessment paradigm. In this paper, a multi-path parallel deep convolutional neural network was proposed for arrhythmia classification. Furthermore, a global average RR interval was introduced to address the issue of similarities between N vs. S categories, and a weighted loss function was developed to solve the imbalance problem using the dynamically adjusted weights based on the proportion of each class in the input batch. The MIT-BIH arrhythmia dataset was used to validate the classification performances of the proposed method. Experimental results under the intra-patient evaluation paradigm and inter-patient evaluation paradigm showed that the proposed method could achieve better classification results than other methods. Among them, the accuracy, average sensitivity, average precision, and average specificity under the intra-patient paradigm were 98.73%, 94.89%, 89.38%, and 98.24%, respectively. The accuracy, average sensitivity, average precision, and average specificity under the inter-patient paradigm were 91.22%, 89.91%, 68.23%, and 95.23%, respectively.

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