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

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  • Fahimeh Nasimi
  • Mohammadreza Yazdchi

Abstract

Differentiating between shockable and non-shockable Electrocardiogram (ECG) signals would increase the success of resuscitation by the Automated External Defibrillators (AED). In this study, a Deep Neural Network (DNN) algorithm is used to distinguish 1.4-second segment shockable signals from non-shockable signals promptly. The proposed technique is frequency-independent and is trained with signals from diverse patients extracted from MIT-BIH, MIT-BIH Malignant Ventricular Ectopy Database (VFDB), and a database for ventricular tachyarrhythmia signals from Creighton University (CUDB) resulting, in an accuracy of 99.1%. Finally, the raspberry pi minicomputer is used to load the optimized version of the model on it. Testing the implemented model on the processor by unseen ECG signals resulted in an average latency of 0.845 seconds meeting the IEC 60601-2-4 requirements. According to the evaluated results, the proposed technique could be used by AED’s.

Suggested Citation

  • Fahimeh Nasimi & Mohammadreza Yazdchi, 2022. "LDIAED: A lightweight deep learning algorithm implementable on automated external defibrillators," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-12, February.
    • Handle: RePEc:plo:pone00:0264405
    DOI: 10.1371/journal.pone.0264405
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