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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence
  • Cardiology

Background:

  • Electrocardiograms (ECG) are crucial for diagnosing cardiovascular diseases (CVD).
  • Standard 12-lead ECGs are inconvenient; wearable single-lead ECG devices offer a practical alternative but have an information gap.
  • Reconstructing 12-lead ECG from single-lead ECG is essential for leveraging wearable technology.

Purpose of the Study:

  • To develop a method for reconstructing 12-lead ECG from arbitrary single-lead ECG signals.
  • To introduce a comprehensive evaluation benchmark (ECGGenEval) for assessing the quality of reconstructed ECGs.
  • To validate the performance of the proposed Multi-Channel Masked Autoencoder (MCMA) model.

Main Methods:

  • Proposed a Multi-Channel Masked Autoencoder (MCMA) for reconstructing 12-lead ECG from single-lead ECG.
  • Developed ECGGenEval, a benchmark for signal-level, feature-level, and diagnostic-level evaluations.
  • Evaluated MCMA performance using metrics such as Mean Square Error (MSE), Pearson correlation, heart rate variability, and F1-scores.

Main Results:

  • MCMA achieved state-of-the-art performance in reconstructing 12-lead ECG.
  • Signal-level evaluation showed low MSE (0.0175, 0.0654) and high Pearson correlation (0.7772, 0.7287).
  • Feature-level and diagnostic-level evaluations demonstrated high accuracy and reliability, with average F1-scores of 0.8233 and 0.8410.

Conclusions:

  • The proposed MCMA effectively reconstructs 12-lead ECG from single-lead ECG, bridging the information gap.
  • The ECGGenEval benchmark provides a robust framework for evaluating ECG reconstruction methods.
  • This approach enhances the utility of wearable single-lead ECG devices for CVD diagnosis.