Why the study?
The most accurate AED shock decision algorithms are based on deep learning, but these algorithms had not been trained and tested using out-of-hospital cardiac arrest data.
Do novel deep learning architectures based on convolutional and residual networks improve shock decision accuracy for AEDs in OHCA patients?
Do novel deep learning architectures based on convolutional and residual networks improve shock decision accuracy for AEDs in OHCA patients?
Novel deep learning architectures for AED shock decision algorithms achieve high accuracy and meet AHA requirements even with 1-second EKG segments, potentially optimizing combined defibrillation and CPR therapy.
Novel DL AED algorithms trained on OHCA data may improve shock decisions; hypothesis-generating and requires prospective validation before adoption.
Automated External Defibrillators (AED) incorporate a shock decision algorithm that analyzes the patient's electrocardiogram (EKG), allowing lay persons to provide life saving defibrillation therapy to out-of-hospital cardiac arrest (OHCA) patients. The most accurate shock decision algorithms are based on deep learning, but these algorithms have not been trained and tested using OHCA data. In this study we propose novel deep learning architectures for shock decision algorithms based on convolutional and residual networks. EKG electronic recordings from a cohort of 852 OHCA cases (4216 AED EKG analyses) were used in the study. EKGs were annotated by a pool of six expert clinicians resulting in 3718 nonshockable and 498 shockable EKGs. Data were partitioned patient wise in a stratified way to train and test the models using 10-fold cross validation, and the procedure was repeated 100 times for statistical evaluation. Performance was assessed using sensitivity (shockable), specificity (non-shockable) and accuracy, and the analysis was conducted for EKG segments of decreasing duration. The best model had median (interdecile range) accuracies of 98.6 (98.5-98.7)%, 98.4 (98.2-98.6)%, 98.2 (97.9-98.4)%, and 97.6 (97.4-97.8)%, for 4, 3, 2 and 1 second EKG segments, respectively. The minimum 90% sensitivity and 95% specificity requirements established by the American Heart Association for shock decision algorithms were met, and the best model presented significantly greater accuracy (p<; 0.05 McNemar test) than previous deep learning solutions for all segment durations. Moreover, the first AHA compliant shock decision algorithm using 1-s segments was demonstrated. This should contribute to a combined optimization of defibrillation and cardiopulmonary resuscitation therapy to improve OHCA survival.
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Jaureguibeitia et al. (2020) studied this question.
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