Research Article
Myocardial Infarction Prediction Using Deep Learning
@INPROCEEDINGS{10.1007/978-3-031-32029-3_13, author={Catarina Cruz and Argentina Leite and E. J. Solteiro Pires and L. Torres Pereira}, title={Myocardial Infarction Prediction Using Deep Learning}, proceedings={Wireless Mobile Communication and Healthcare. 11th EAI International Conference, MobiHealth 2022, Virtual Event, November 30 -- December 2, 2022, Proceedings}, proceedings_a={MOBIHEALTH}, year={2023}, month={5}, keywords={Electrocardiogram Myocardial infarction Deep Learning Long Short-Term Memory}, doi={10.1007/978-3-031-32029-3_13} }- Catarina Cruz
Argentina Leite
E. J. Solteiro Pires
L. Torres Pereira
Year: 2023
Myocardial Infarction Prediction Using Deep Learning
MOBIHEALTH
Springer
DOI: 10.1007/978-3-031-32029-3_13
Abstract
Myocardial infarction, known as heart attack, is one of the leading causes of world death. It occurs when blood heart flow is interrupted by part of coronary artery occlusion, causing the ischemic episode to last longer, creating a change in the patient’s ECG. In this work, a method was developed for predicting patients with MI through Frank 3-lead ECG extracted from Physionet’s PTB ECG Diagnostic Database and using instantaneous frequency and spectral entropy to extract features. Two neural networks were applied: Long Short-Term Memory and Bi-Long Short-Term Memory, obtaining a better result with the first one, with an accuracy of 78%.
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