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Detection and diagnosis of myocarditis in young patients using ECG analysis based on artificial neural networks

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Abstract

Myocarditis is a common disease of the pediatric circulatory system, and its clinical manifestations are diversified. Especially in older patients, chest tightness, shortness of breath and palpitations may occur. However, some patients may have no distinct clinical symptoms and may occasionally experience symptoms during the diagnosis and treatment of respiratory infections, such as arrhythmias in infants and young patients. Because the clinical manifestations of myocarditis are not typical, the specificity and sensitivity of serum myocardial enzyme detection are not very high, which hinders a definitive diagnosis of certain cases by clinicians. However, degeneration, necrosis and inflammatory diseases of cardiomyocytes can affect myocardial conductivity and stress. In patients with myocarditis, an electrocardiogram (ECG) signal changes abnormally; thus, the ECG remains an indispensable means of diagnosing myocarditis. Biomedical signal detection and signal processing techniques can facilitate the study of biological and physiological systems and assist physicians in the diagnosis and treatment of patients. However, traditional manual analysis of ECG methods has some drawbacks. Due to the extensive variety of electrocardiograms, variations in various waveform patterns are subtle and complex. Clinicians often need a solid theoretical foundation and a wealth of clinical experience to make a correct assessment using an ECG. If the medical expert is engaged in a large number of ECG recognition studies for a long time, fatigue may develop and cause misdiagnosis. In addition, the manual analysis of an ECG is part of the postmortem analysis, achieving a real-time diagnosis is difficult, and the resulting delay is likely to cause critical patients to miss the best treatment opportunity. To solve this problem, we have developed a dynamic electrocardiogram medical-assisted diagnosis system to assist medical experts in the diagnosis and treatment of patients with myocarditis. Our system can process the collected ECG signals, obtain waveform parameters, and automatically perform an analysis according to an expert’s clinical experience. These parameters provide diagnostic recommendations.

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Acknowledgements

This work was supported in part by the National Key R&D Program (No. 2018YFC0831006-3 and 2017YFB1400102), the Key Research and Development Plan of Shandong Province (No. 2017CXGC1503 and 2018GSF118228).

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  1. Yujun Li and Meijun Yang contributed equally to the paper.

Authors and Affiliations

  1. School of Information Science and Engineering, Shandong University, Qingdao, 266237, China

    Yujun Li, Meijun Yang & Zhi Liu

  2. School of Physics and Electronics, Shandong Normal University, Jinan, 250100, China

    Yuefeng Zhao

  3. School of Electronic Information, Qingdao University, Qingdao, 266071, China

    Dongmei Jiang

  4. School of Software, Shandong University, Jinan, 250101, China

    Lizhen Cui

  5. State Key Laboratory of ASIC and Systems, School of Microelectronics, Fudan University, Shanghai, 200433, China

    Mingyu Wang

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  1. Yujun Li
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  3. Zhi Liu
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  4. Yuefeng Zhao
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Correspondence to Zhi Liu or Mingyu Wang.

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Li, Y., Yang, M., Liu, Z. et al. Detection and diagnosis of myocarditis in young patients using ECG analysis based on artificial neural networks. Computing 102, 1–18 (2020). https://doi.org/10.1007/s00607-019-00725-4

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