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Transfer Learning in ECG Classification from Human to Horse Using a Novel Parallel Neural Network Architecture.

Glenn Van Steenkiste1, Gunther van Loon2, Guillaume Crevecoeur3,4

  • 1Department of large animal internal medicine, Ghent University, Ghent, 9000, Belgium. Glenn.VanSteenkiste@ugent.be.

Scientific Reports
|January 15, 2020
PubMed
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Researchers developed new methods for analyzing equine electrocardiograms (eECGs) using wavelet transforms and a deep neural network. This advances automated eECG interpretation, crucial for equine cardiac health diagnostics.

Area of Science:

  • Veterinary Cardiology
  • Biomedical Signal Processing
  • Machine Learning in Healthcare

Background:

  • Equine electrocardiogram (eECG) analysis is challenging due to unique cardiac morphology and innervation, rendering human/small animal software unreliable.
  • Current literature lacks robust methods for eECG filtering, beat detection, and classification, with no public eECG databases available.
  • Automated eECG analysis is essential for accurate equine cardiac diagnostics.

Purpose of the Study:

  • To develop and validate advanced signal processing techniques for equine electrocardiograms.
  • To introduce a novel deep neural network for robust eECG beat classification.
  • To establish a foundation for automated eECG interpretation in veterinary medicine.

Main Methods:

  • Wavelet transforms were employed for eECG filtering and QRS complex detection.

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  • A novel deep neural network with a parallel convolutional architecture was designed for eECG beat classification.
  • The network was trained and optimized using a genetic algorithm on the MIT-BIH arrhythmia and a custom eECG dataset (26,440 beats, 4 classes).
  • Transfer learning was applied from the MIT-BIH dataset to the eECG dataset.

    • Main Results:

    • Wavelet transforms provided effective filtering and QRS detection for eECGs.
    • The deep neural network achieved high accuracy: 97.7% on MIT-BIH and 92.6% on the eECG dataset.
    • Following transfer learning, the average accuracy, recall, precision, and F1 score improved, reaching 97.1% on the eECG dataset.
    • The model demonstrated robust classification of normal, premature ventricular contraction, premature atrial contraction, and noise.

    Conclusions:

    • Wavelet transforms and a novel deep neural network offer a promising solution for automated eECG analysis.
    • The developed methods significantly improve the accuracy and reliability of eECG interpretation.
    • This work lays the groundwork for developing clinical diagnostic tools for equine cardiac conditions.