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Multi-Lead ECG Classification via an Information-Based Attention Convolutional Neural Network

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Abstract

A novel structure based on channel-wise attention mechanism is presented in this paper. Embedding with the proposed structure, an efficient classification model that accepts multi-lead electrocardiogram (ECG) as input is constructed. One-dimensional convolutional neural networks (CNNs) have proven to be effective in pervasive classification tasks, enabling the automatic extraction of features while classifying targets. We implement the residual connection and design a structure which can learn the weights from the information contained in different channels in the input feature map during the training process. An indicator named mean square deviation is introduced to monitor the performance of a particular model segment in the classification task on the two out of the five ECG classes. The data in the MIT-BIH arrhythmia database is used and a series of control experiments is conducted. Utilizing both leads of the ECG signals as input to the neural network classifier can achieve better classification results than those from using single channel inputs in different application scenarios. Models embedded with the channel-wise attention structure always achieve better scores on sensitivity and precision than the plain Resnet models. The proposed model exceeds the performance of most of the state-of-the-art models in ventricular ectopic beats (VEB) classification and achieves competitive scores for supraventricular ectopic beats (SVEB). Adopting more lead ECG signals as input can increase the dimensions of the input feature maps, helping to improve both the performance and generalization of the network model. Due to its end-to-end characteristics, and the extensible intrinsic for multi-lead heart diseases diagnosing, the proposed model can be used for the realtime ECG tracking of ECG waveforms for Holter or wearable devices.

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Authors and Affiliations

  1. School of Biomedical Engineering,, Shanghai Jiao Tong University, Shanghai, 200240, China

    Hao Tung  (董 昊)

  2. Shukun (Beijing) Network Technology Co., Ltd., Beijing, 100190, China

    Chao Zheng  (郑 超) & Xinsheng Mao  (毛新生)

  3. Institute of Medical Robotic, Shanghai Jiao Tong University, Shanghai, 200240, China

    Dahong Qian  (钱大宏)

Authors
  1. Hao Tung  (董 昊)
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  2. Chao Zheng  (郑 超)
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  3. Xinsheng Mao  (毛新生)
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  4. Dahong Qian  (钱大宏)
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Corresponding author

Correspondence to Dahong Qian  (钱大宏).

Additional information

Foundation item: the Key Research and Development Project of Zhejiang Province, China (No. 2017C03029)

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Tung, H., Zheng, C., Mao, X. et al. Multi-Lead ECG Classification via an Information-Based Attention Convolutional Neural Network. J. Shanghai Jiaotong Univ. (Sci.) 27, 55–69 (2022). https://doi.org/10.1007/s12204-021-2371-8

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  • DOI: https://doi.org/10.1007/s12204-021-2371-8

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