Key result
A 1D-CNN model outperformed an RNN model for classifying shockable versus non-shockable cardiac arrest rhythms across all ECG segments (accuracy 91.3% vs 50.6%).
Why the study?
Deep learning models needed to be developed and evaluated for cardiac arrest rhythm classification during CPR.
Does a 1D-CNN model improve cardiac arrest rhythm classification accuracy compared to an RNN model during CPR?
Does a 1D-CNN model improve cardiac arrest rhythm classification accuracy compared to an RNN model during CPR?
A 1D-CNN model demonstrated superior accuracy over an RNN model for classifying cardiac arrest rhythms, even in the presence of CPR compression artifacts.
To develop and evaluate deep learning models for cardiac arrest rhythm classification during cardiopulmonary resuscitation (CPR), we analyzed 508 electrocardiogram (ECG) segments (each 4 s in duration, recorded at 250 Hz) from 131 cardiac arrest patients. Compression-affected segments were recorded during chest compressions, while non-compression segments were extracted during compression pauses or immediately after return of spontaneous circulation (ROSC) declaration. One-dimensional convolutional neural network (1D-CNN) and recurrent neural network (RNN) models were employed for four binary classification tasks: (1) shockable rhythms (VF and pVT) versus non-shockable rhythms (asystole and PEA) in all ECG segments; (2) the same classification in compression-affected ECG segments; (3) pulse-generating rhythms (ROSC rhythm) versus non-pulse-generating rhythms (asystole, PEA, VF and pVT) in all ECG segments; and (4) the same classification in compression-affected ECG segments. The 1D-CNN model consistently outperformed the RNN model across all classification tasks. For shockable versus non-shockable rhythm classification, the 1D-CNN achieved accuracies of 91.3% and 89.8% for all ECG segments and compression-affected ECG segments, respectively, compared to 50.6% and 54.5% for the RNN. In detecting pulse-generating rhythms, the 1D-CNN demonstrated accuracies of 90.9% and 85.7% for all ECG segments and compression-affected ECG segments, respectively, while the RNN achieved 92.2% and 84.4%. The 1D-CNN model demonstrated superior performance in cardiac arrest rhythm classification, maintaining high accuracy even with compression-affected ECG data.
No takes yet. Share an insight, caveat, or question.
Lee et al. (2025) studied Cardiac arrest (n=131). 1D-CNN model vs. RNN model was evaluated on Accuracy for shockable versus non-shockable rhythm classification in all ECG segments. A 1D-CNN model outperformed an RNN model for classifying shockable versus non-shockable cardiac arrest rhythms across all ECG segments (accuracy 91.3% vs 50.6%).
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: