Volume 30 Issue 5
Sep.  2021
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LIU Jikui, WANG Ruxin, WEN Bo, LIU Zengding, MIAO Fen, LI Ye. Myocardial Infarction Detection and Localization with Electrocardiogram Based on Convolutional Neural Network[J]. Chinese Journal of Electronics, 2021, 30(5): 833-842. doi: 10.1049/cje.2021.06.005
Citation: LIU Jikui, WANG Ruxin, WEN Bo, LIU Zengding, MIAO Fen, LI Ye. Myocardial Infarction Detection and Localization with Electrocardiogram Based on Convolutional Neural Network[J]. Chinese Journal of Electronics, 2021, 30(5): 833-842. doi: 10.1049/cje.2021.06.005

Myocardial Infarction Detection and Localization with Electrocardiogram Based on Convolutional Neural Network

doi: 10.1049/cje.2021.06.005
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This work is supported by the the National Natural Science Foundation of China (No.61771465, No.U1801261, No.81701788), Strategic Priority CAS Project (No.XDB38040200), and Shenzhen Science and Technology Program (No.JCYJ20180703145002040).

  • Received Date: 2020-07-31
    Available Online: 2021-09-02
  • Electrocardiogram (ECG) is widely used in Myocardial infarction (MI) diagnosis. The automatic diagnosis of MI based on the 12-lead ECG needs to consider not only the waveform change features in multi-resolution time series, but also the spatial correlation information between the leads. To this end, this work proposed multiscale spatiotemporal feature extraction method based on Convolutional neural network (CNN) for MI automatic diagnosis. First, the 12-lead ECG is first transformed into an ECG image through wavelet decomposition and 3-dimensional space reconstruction. The MI-CNN model is then constructed to identify MI using 41368 ECG images. Finally, we develop the LL-CNN model, which is utilized only after the ECG signal is identified as an MI event by the MI-CNN model, to localize MI by employing transfer learning to overcome the limited data problem. The proposed method has achieved an accuracy of 99.51% on MI detection, and a macro-F1 of 99.14% on MI localization. Moreover, the features visualization shows that U-wave has significant diagnostic value for MI. The proposed method significantly improves the performance of MI detection and localization compared with other methods. It is promising to be used for MI monitoring and diagnosis.
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