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LDIAED: A lightweight deep learning algorithm implementable on automated external defibrillators.

Fahimeh Nasimi1, Mohammadreza Yazdchi1

  • 1Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran.

Plos One
|February 25, 2022
PubMed
Summary
This summary is machine-generated.

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A Deep Neural Network (DNN) algorithm accurately distinguishes shockable from non-shockable Electrocardiogram (ECG) signals. This advancement enhances Automated External Defibrillator (AED) resuscitation success rates with rapid, reliable analysis.

Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Medicine
  • Cardiology

Background:

  • Automated External Defibrillators (AEDs) are crucial for cardiac arrest resuscitation.
  • Accurate differentiation of shockable versus non-shockable Electrocardiogram (ECG) rhythms is critical for AED efficacy.
  • Current limitations exist in prompt and precise ECG signal analysis for AEDs.

Purpose of the Study:

  • To develop and evaluate a Deep Neural Network (DNN) algorithm for rapid classification of shockable and non-shockable ECG signals.
  • To assess the performance of the DNN model in terms of accuracy and latency for real-time AED application.
  • To validate the model's applicability on a compact computing platform meeting medical device standards.

Main Methods:

  • A Deep Neural Network (DNN) algorithm was designed to analyze 1.4-second ECG signal segments.

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  • The model was trained using diverse ECG datasets from MIT-BIH, Ventricular Fibrillation Database (VFDB), and Creighton University Database (CUDB).
  • The optimized DNN model was deployed on a Raspberry Pi minicomputer for real-time performance testing.

    • Main Results:

    • The DNN algorithm achieved a high accuracy of 99.1% in distinguishing shockable from non-shockable ECG signals.
    • The implemented model demonstrated an average latency of 0.845 seconds on the Raspberry Pi, meeting IEC 60601-2-4 standards.
    • The frequency-independent nature of the algorithm ensures robustness across various signal conditions.

    Conclusions:

    • The proposed DNN-based technique offers a highly accurate and prompt method for ECG rhythm classification.
    • The successful deployment on a Raspberry Pi indicates the potential for integration into next-generation AED devices.
    • This advancement could significantly improve the effectiveness of emergency cardiac resuscitation protocols.