Intelligent Detection of Silent Myocardial Ischemia Dynamic Electrocardiogram Based on Deep Learning

Corresponding author: miaoyuanqing

DOI: 10.12201/bmr.202111.00009

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  Abstract: ECG is one of the most commonly used examinations in clinical diagnosis, which is widely used. ECG can record the electrical activity of the heart, not only assist clinical diagnosis of arrhythmia, myocardial ischemia, myocardial infarction and other diseases, but also judge the impact of drugs or electrolytes on the heart. Deep learning can be learned through training data sets, and a lot of useful information can be obtained in the learning process, which is very helpful for understanding and judging image, sound, text and other data. Based on deep learning technology, this paper proposes an algorithm to assist doctors in intelligent analysis of silent myocardial ischemia dynamic electrocardiogram, which greatly improves the accuracy of dynamic electrocardiogram analysis and reduces the misdiagnosis rate of ECG interpretation.

  Key words: deep; learning, dynamic; electrocardiogram, intelligent; analysis, silent; myocardial ischemia

  Submit time: 23 December 2021

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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