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Related Concept Videos

Correlation between ECG and Cardiac Cycle01:25

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The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...
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Related Experiment Video

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Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
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Uncertainty quantification in DenseNet model using myocardial infarction ECG signals.

V Jahmunah1, E Y K Ng1, Ru-San Tan2

  • 1School of Mechanical and Aerospace Engineering, Nanyang Technological University.

Computer Methods and Programs in Biomedicine
|December 19, 2022
PubMed
Summary
This summary is machine-generated.

A new DenseNet model reliably detects myocardial infarction (MI) from ECGs, even with noise. It accurately communicates diagnostic uncertainty, ensuring trustworthy AI for emergency healthcare applications.

Keywords:
Deep learningDenseNet modelMyocardial infarctionPredictive entropyReverse KL divergenceUncertainty quantification

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Area of Science:

  • Artificial Intelligence in Medicine
  • Biomedical Signal Processing
  • Machine Learning for Diagnostics

Background:

  • Myocardial infarction (MI) diagnosis relies on electrocardiograms (ECGs), but noise can impede automated analysis.
  • Quantifying model uncertainty is crucial for reliable AI-driven ECG interpretation.

Purpose of the Study:

  • Develop and evaluate a Dirichlet DenseNet model for robust MI detection from ECGs.
  • Assess the model's ability to identify and quantify uncertainty in noisy ECG signals.
  • Ensure trustworthy AI for critical healthcare applications like emergency MI diagnosis.

Main Methods:

  • A Dirichlet DenseNet model was trained on ECGs from the PTB database.
  • The model was tested using synthesized ECGs with added electromagnetic (em) and motion artifact (ma) noise.
  • Predictive entropy served as the uncertainty measure, with performance evaluated using UNSE, UNSP, UNAC, and UNPR metrics.

Main Results:

  • The model's uncertainty sensitivity (UNSE) increased with decreasing noise levels.
  • High uncertainty accuracy (UNAC) was achieved for both em (80%) and ma (82.4%) noise at specific signal-to-noise ratios (SNRs).
  • Uncertainty specificity (UNSP) and precision (UNPR) approached 100%, indicating strong self-awareness of prediction confidence.

Conclusions:

  • The developed DenseNet model reliably conveys diagnostic uncertainty, enhancing trust in AI for ECG analysis.
  • The model's ability to handle noisy data and communicate confidence makes it suitable for emergency MI diagnosis.
  • This research supports the integration of AI tools in clinical settings for improved patient outcomes.