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Myocardial infarction is one of the main reasons of death worldwide. Myocardial infarction detection can be performed through the use of Multilayer Perceptron Neural Networks (MLP) using Reconstructed Phase Space (REF) images. The objective of the present work is to compare the ECG and VCG with Reconstructed Phase Space and signal segmentation to classify infarction signals, for which the best result obtained in this work was an accuracy of 0.7754, recall of 0.9348, precision of 0.7745 and F1-Score of 0.8468.

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Identification of Myocardial Infarction Using an MLP Network and the Phase Space of ECG and VCG Signals

  • 会议论文

  • 发表日期：

    2025年

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  • Emanuel T. de A. da Silva,
  • Gabriel D. Gomes,
  • Rafael D. de Sousa,
  • Villeneve O. Soares,
  • Pedro A. de A. da Silva,
  • Carlos D. M. Regis

摘要

Myocardial infarction is one of the main reasons of death worldwide. Myocardial infarction detection can be performed through the use of Multilayer Perceptron Neural Networks (MLP) using Reconstructed Phase Space (REF) images. The objective of the present work is to compare the ECG and VCG with Reconstructed Phase Space and signal segmentation to classify infarction signals, for which the best result obtained in this work was an accuracy of 0.7754, recall of 0.9348, precision of 0.7745 and F1-Score of 0.8468.