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Electrocardiogram Fundamentals01:28

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Introduction
An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
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Multi-label classification of reduced-lead ECGs using an interpretable deep convolutional neural network.

Nima L Wickramasinghe1, Mohamed Athif2

  • 1Department of Electronic and Telecommunication Engineering, University of Moratuwa, Sri Lanka.

Physiological Measurement
|May 26, 2022
PubMed
Summary

This study introduces a deep learning model for identifying 26 cardiac abnormalities from reduced lead Electrocardiograms (ECGs). The model achieves performance comparable to 12-lead ECGs, even with noisy signals.

Keywords:
CNNECGSHAPclassificationdeep learninginterpretablemulti-label

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Biomedical Signal Processing

Background:

  • Electrocardiograms (ECGs) are crucial for diagnosing cardiac abnormalities.
  • Reduced lead ECGs offer potential for more accessible cardiac monitoring.
  • Interpreting complex ECG data remains a challenge for automated systems.

Purpose of the Study:

  • To develop and interpret a multi-label classification model for 26 cardiac abnormalities using reduced lead ECGs.
  • To evaluate the model's performance against traditional 12-lead ECG analysis.
  • To assess the model's robustness under noisy conditions and provide interpretability.

Main Methods:

  • Utilized PhysioNet/CinC challenge 2021 datasets for training.
  • Preprocessed ECG recordings (normalization, resampling, zero-padding).
  • Employed deep convolutional neural networks processing both time and frequency domains, combined with SHapley Additive exPlanations (SHAP) for interpretation.

Main Results:

  • Achieved high rankings in the PhysioNet/CinC challenge 2021 across various lead configurations (2- to 12-lead).
  • Demonstrated robust performance on noisy ECG signals, maintaining good mean F1 scores.
  • SHAP analysis validated model performance and identified labeling inconsistencies.

Conclusions:

  • A novel deep learning model accurately detects 26 cardiac abnormalities from reduced lead ECGs.
  • The model's accuracy is comparable to 12-lead ECGs, offering a more efficient diagnostic tool.
  • Model interpretability through SHAP provides insights into diagnostic reasoning and potential data issues.