Optimization of End-to-End Convolutional Neural Networks for Analysis of Out-of-Hospital Cardiac Arrest Rhythms during Cardiopulmonary Resuscitation

Abstract

High performance of the shock advisory analysis of the electrocardiogram (ECG) during cardiopulmonary resuscitation (CPR) in out-of-hospital cardiac arrest (OHCA) is important for better management of the resuscitation protocol. It should provide fewer interruptions of chest compressions (CC) for non-shockable organized rhythms (OR) and Asystole, or prompt CC stopping for early treatment of shockable ventricular fibrillation (VF). Major disturbing factors are strong CC artifacts corrupting raw ECG, which we aimed to analyze with optimized end-to-end convolutional neural network (CNN) without pre-filtering or additional sensors. The hyperparameter random search of 1500 CNN models with 2–7 convolutional layers, 5–50 filters and 5–100 kernel sizes was done on large databases from independent OHCA interventions for training (3001 samples) and validation (2528 samples). The best model, named CNN3-CC-ECG network with three convolutional layers (filters@kernels: 5@5,25@20,50@20) presented Sensitivity Se(VF) = 89%(268/301), Specificity Sp(OR) = 91.7%(1504/1640), Sp(Asystole) = 91.1%(3325/3650) on an independent test OHCA database. CNN3-CC-ECG’s ability to effectively extract features from raw ECG signals during CPR was comprehensively demonstrated, and the dependency on the CPR corruption level in ECG was tested. We denoted a significant drop of Se(VF) = 74.2% and Sp(OR) = 84.6% in very strong CPR artifacts with a signal-to-noise ratio of SNR < −9 dB, p < 0.05. Otherwise, for strong, moderate and weak CC artifacts (SNR > −9 dB, −6 dB, −3 dB), we observed insignificant performance differences: Se(VF) = 92.5–96.3%, Sp(OR) = 93.4–95.5%, Sp(Asystole) = 92.6–94.0%, p > 0.05. Performance stability with respect to CC rate was validated. Generalizable application of the optimized computationally efficient CNN model was justified by an independent OHCA database, which to our knowledge is the largest test dataset with real-life cardiac arrest rhythms during CPR.

A Modified Deep Learning Framework for Arrhythmia Disease Analysis in Medical Imaging Using Electrocardiogram Signal

“…This must deliver fewer intervals of chest compressions for unshocking organized rhythms and systole or rapid CC cessation for medical intervention of defibrillation ventricular fibrillation. To identify heart attack during CPR, Jekova and Krasteva [ 10 ] utilized a deep learning model termed CNN3-CCECG, which they verified by using impartial dataset OHCA. A hyperparameter randomized search of 1500 CNN models was done on huge datasets from various sources.…”

Automated Condition-Based Suppression of the CPR Artifact in ECG Data to Make a Reliable Shock Decision for AEDs during CPR

“…Recently, methods have been introduced based on deep learning approaches to directly analyze CPR-contaminated ECG rhythms without filtering the CPR artifact [17,28]. These algorithms provide a relatively accurate shock versus no-shock classification.…”

“…As shown, there is a notable SE improvement for all SNR levels when our proposed filtering algorithm is applied. For example, for an SNR of −9 dB (which can be considered as a severe corruption level [28]) the SE increased from 56.2% to 85% (see Figure 9a). For both pre-and postfiltering approaches, the differences in their performances for SP values for all SNR were negligible, albeit they all show decreased values with decreasing SNR (see Figure 9b).…”

Role of artificial intelligence in defibrillators: a narrative review

“…Jekova et al aimed to optimise an end-to-end CNN model for shock advisory decision during CPR using real-life AED recordings in OHCA. 26 Their CNN was able to extract features from raw ECGs during CPR with sensitivities and specificities of 89.0% and 91.7%, respectively. They tested their model on 5591 real-life cardiac arrest rhythms during CPR.…”