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Ensemble classification combining ResNet and handcrafted features with three-steps training.

Guadalupe Garcia-Isla1, Federico M Muscato1, Andrea Sansonetti1

  • 1Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Piazza Leonardo da Vinci, 32 , Milano.

Physiological Measurement
|September 2, 2022
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Summary
This summary is machine-generated.

This study developed an ECG classifier integrating deep learning and traditional features. The best results for 12-lead and 2-lead ECG classification were achieved using a novel 3-step training procedure.

Keywords:
deep learningelectrocardiogramelectrophysiologymachine learning

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Area of Science:

  • Cardiology
  • Machine Learning
  • Signal Processing

Background:

  • Electrocardiogram (ECG) analysis is crucial for diagnosing cardiac conditions.
  • Integrating diverse data types can improve ECG classification accuracy.

Purpose of the Study:

  • To develop an ECG classifier for variable leads using a hybrid deep learning and classic machine learning approach.
  • To explore optimal model architecture and training procedures for ECG classification.

Main Methods:

  • A modified ResNet with attention mechanisms formed the deep branch, processing windowed ECG signals.
  • A wide branch integrated 20 handcrafted cardiac rhythm features.
  • An ensemble model combined multiple trained models using majority voting.
  • A three-step training procedure (D+W+D) was investigated.

Main Results:

  • The D+W+D training procedure yielded the best performance.
  • Challenge metrics of 0.709 for 12-lead and 0.677 for 2-lead ECG models were achieved.
  • The study utilized a dataset of 84,210 ECG recordings.

Conclusions:

  • Integrating handcrafted features with deep learning enhances ECG classification generalization.
  • This approach provides a method to incorporate explicit information into classification decisions.
  • This is the first study to investigate training procedures for integrating both feature types in ECG classification.