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A novel lightweight computerized ECG interpretation approach based on clinical 12-lead data

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Abstract

Although 12-lead electrocardiograms (ECGs) provide a wide range of spatiotemporal characteristics, interpreting them for arrhythmia detection is difficult due to a lack of reliable large-scale clinical datasets. Herein, we proposed an innovative lightweight computerized ECG interpretation approach based on 12-lead data. Our model was trained, validated, and tested on 53845 standard 12-lead ECG records collected at Shanghai First People’s Hospital in affiliation with Shanghai Jiao Tong University. The experiments revealed that our approach had a classification accuracy of 94.41% in the classification task of seven types of rhythms, which was markedly superior to related single-lead and 12-lead ECG classification methods. Moreover, the average receiver operating characteristic area under the curve reached a value of 0.940, and the precision values for sinus tachycardia and sinus bradycardia were 0.945 and 0.91, respectively, with specificity values of 0.996 and 0.994. By employing our boosting method, we were able to improve the accuracy to 94.85%. To investigate the performance degradation of the proposed neural network in some classes, an ECG cardiologist was enlisted to review questionable ECGs; this process provides a promising direction for network performance improvement. Therefore, the proposed computerized ECG interpretation approach has practical significance because it could help professional physicians analyze patients’ heart conditions based on real-time 12-lead ECG or grade their disease severity in advance.

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Author information

Authors and Affiliations

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

    YunQing Liu, ChengJin Qin, JinLei Liu, YanRui Jin, ZhiYuan Li & ChengLiang Liu

  2. Key Lab of Artificial Intelligence (Ministry of Education), AI Institute, Shanghai Jiao Tong University, Shanghai, 200240, China

    YunQing Liu, ChengJin Qin, JinLei Liu, YanRui Jin, ZhiYuan Li & ChengLiang Liu

  3. Department of Cardiology, Shanghai First People’s Hospital Affiliated to Shanghai Jiao Tong University, Shanghai, 200080, China

    LiQun Zhao

Authors
  1. YunQing Liu
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  2. ChengJin Qin
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  3. JinLei Liu
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  4. YanRui Jin
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  5. ZhiYuan Li
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  6. LiQun Zhao
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  7. ChengLiang Liu
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Corresponding authors

Correspondence to ChengJin Qin or ChengLiang Liu.

Additional information

This work was supported by Shanghai Municipal Science and Technology Major Project (Grant No. 2021SHZDZX0102) and the National Key Technology R&D Program of China (Grant No. SQ2018YFB130700).

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Liu, Y., Qin, C., Liu, J. et al. A novel lightweight computerized ECG interpretation approach based on clinical 12-lead data. Sci. China Technol. Sci. 67, 449–463 (2024). https://doi.org/10.1007/s11431-023-2460-2

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  • DOI: https://doi.org/10.1007/s11431-023-2460-2

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