Abstract 238: A Deep Neural Network Approach for Continuous Electrocardiogram-based Shock Advisory System During Cardio Pulmonary Resuscitation

• Published in: Circulation (New York, N.Y.) Vol. 142; no. Suppl_4
• Main Authors: Hajeb Mohammadalipour, Shirin, Cascella, Alicia, Valentine, Matt, Chon, K.H.
• Format: Journal Article
• Language: English
• Published: 17.11.2020
• ISSN: 0009-7322; 1524-4539
• DOI: 10.1161/circ.142.suppl_4.238

Abstract only The ability of an automatic external defibrillator (AED) to make a reliable shock decision during cardio pulmonary resuscitation (CPR) would improve the survival rate of patients with out-of-hospital cardiac arrest. Since chest compressions induce motion artifacts in the electrocardiogram (ECG), current AEDs instruct the user to stop CPR while an automated rhythm analysis is performed. It has been shown that minimizing interruptions in CPR increases the chance of survival. While deep learning approaches have been used successfully for arrhythmia classification, their performance has not been evaluated for creating an AED shock advisory system that can coexist with CPR. To this end, the objective of this study was to apply a deep-learning algorithm using convolutional layers and residual networks to classify shockable versus non-shockable rhythms in the presence and absence of CPR artifact using only the ECG data. The feasibility of the deep learning method was validated using 8-sec segments of ECG with and without CPR. Two separate databases were used: 1) 40 subjects’ data without CPR from Physionet with 1131 shockable and 2741 non-shockable classified recordings, and 2) CPR artifacts that were acquired from a commercial AED during asystole delivered by 43 different resuscitators. For each 8-second ECG segment, randomly chosen CPR data from 43 different types were added to it so that 5 non-shockable and 10 shockable CPR-contaminated ECG segments were created. We used 30 subjects’ and the remaining 10 for training and test datasets, respectively, for the database 1). For the database 2), we used 33 and 10 subjects’ data for training and testing, respectively. Using our deep neural network model, the sensitivity and specificity of the shock versus no-shock decision for both datasets using the four-fold cross-validation were found to be 95.21% and 86.03%, respectively. For shockable versus non-shockable classification of ECG without CPR artifact, the sensitivity was 99.04% and the specificity was 95.2%. A sensitivity of 94.21% and a specificity of 86.14% were obtained for ECG with CPR artifact. These results meet the AHA sensitivity requirement (>90%).