Key result
The LDIAED deep learning algorithm distinguished shockable from non-shockable ECG signals with 99.1% accuracy, 96.13% sensitivity, and 99.64% specificity.
Why the study?
Differentiating between shockable and non-shockable ECG signals promptly would increase resuscitation success by automated external defibrillators.
A lightweight deep learning algorithm can accurately and rapidly distinguish shockable from non-shockable ECG rhythms on a low-power processor, meeting IEC 60601-2-4 requirements for automated external defibrillators.
May aid automated defibrillator rhythm analysis; leaves open prospective clinical validation before practice change.
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.
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Nasimi et al. (2022) studied Ventricular Tachyarrhythmia / Cardiac Arrest (n=16,062). LDIAED deep learning algorithm was evaluated on Classification accuracy of shockable vs non-shockable ECG signals. The LDIAED deep learning algorithm distinguished shockable from non-shockable ECG signals with 99.1% accuracy, 96.13% sensitivity, and 99.64% specificity.
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