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Meta-Analysis to Prognosis Myocardial Infarction Using 12 Lead ECG

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High Performance Computing and Networking

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 853))

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Abstract

Cardiovascular disease is caused by heart or blood vessels. It creates immediate heart arrest or a blockage and leads to death. Nearly 70% of people are suffering from these diseases. One of the major diseases that affect the heart is myocardial infarction (MI). MI occurs when there is some damage to the heart muscle. There are various test measures for diagnosing myocardial infarction. A common way to diagnose heart diseases in the medical field is electrocardiogram (ECG). In diagnosing MI through ECG, the changes that occur in the ECG wave are ST-Elevation MI (STEMI), non-ST-elevation MI (NSTEMI), and left bundle branch block (LBBB). In this study, we discuss different classification methods for the diagnosis of MI. Before the classification of the diseases, preprocessing is a preliminary task to achieve accurate information. In comparison with KNN, SVM, and YOLOv3 algorithm, LSTM could give better classification results for the diagnosis of MI. In the future, our research can be carried out on automatic diagnosis of the diseases, by using current trends of deep learning models.

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Authors and Affiliations

  1. School of Information Technology and Engineering (SITE), Vellore Institute of Technology, Vellore, India

    N. Jothiaruna & A. Anny Leema

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  1. N. Jothiaruna
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Editors and Affiliations

  1. Jawaharlal Nehru Technological University, Kakinanda, Andhra Pradesh, India

    Ch. Satyanarayana

  2. Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India

    Debasis Samanta

  3. School of Computing, University of Eastern Finland, Kuopio, Finland

    Xiao-Zhi Gao

  4. Department of Electronics and Communication Engineering, Delhi Technological University, New Delhi, Delhi, India

    Rajiv Kumar Kapoor

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Jothiaruna, N., Leema, A.A. (2022). Meta-Analysis to Prognosis Myocardial Infarction Using 12 Lead ECG. In: Satyanarayana, C., Samanta, D., Gao, XZ., Kapoor, R.K. (eds) High Performance Computing and Networking. Lecture Notes in Electrical Engineering, vol 853. Springer, Singapore. https://doi.org/10.1007/978-981-16-9885-9_39

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  • DOI: https://doi.org/10.1007/978-981-16-9885-9_39

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