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Acute Myocardial Infarction in Rats
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Detecting and interpreting myocardial infarction using fully convolutional neural networks.

Nils Strodthoff1,2, Claas Strodthoff3

  • 1Fraunhofer Heinrich Hertz Institute, 10587 Berlin, Germany.

Physiological Measurement
|December 8, 2018
PubMed
Summary

This study introduces a novel algorithm for detecting myocardial infarction directly from electrocardiogram (ECG) data. The algorithm achieves high accuracy, comparable to human cardiologists, and offers interpretable decision criteria.

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Myocardial infarction (MI) detection relies heavily on electrocardiogram (ECG) interpretation.
  • Current diagnostic methods may involve complex preprocessing steps.
  • Developing automated, accurate, and interpretable ECG analysis tools is crucial for clinical practice.

Purpose of the Study:

  • To develop and evaluate an algorithm for direct ECG analysis for myocardial infarction detection.
  • To investigate the decision-making criteria of the proposed algorithm.
  • To assess the algorithm's performance against established methods and human experts.

Main Methods:

  • An ensemble of fully convolutional neural networks was trained on the PTB ECG dataset.
  • State-of-the-art attribution methods were employed to analyze the network's decisions.
  • A 10-fold cross-validation with patient-based sampling was used for evaluation.

Main Results:

  • The algorithm achieved 93.3% sensitivity and 89.7% specificity for myocardial infarction detection.
  • Performance was comparable to that of human cardiologists.
  • Channel-specific regions influencing the neural network's decisions were identified, aligning with cardiologists' indicators.

Conclusions:

  • Algorithmic ECG analysis shows significant potential for clinical applications.
  • The method provides quantitative performance and interpretable decision criteria for enhanced clinical utility.
  • Per-example assessment of decision criteria improves the comprehensibility of the algorithmic approach.