## 4 Discussion  
This study employed deep learning techniques, specifically 1D-CNN and RNN models, to classify cardiac arrest rhythms using 508 ECG segments from 131 cardiac arrest patients, including both in-hospital and out-of-hospital cases. The 1D-CNN model consistently outperformed the RNN, demonstrating higher test-set accuracies across various binary classifications. Notably, the 1D-CNN achieved test-set accuracies of 91.3% for shockable versus non-shockable rhythms, 89.8% for shockable versus non-shockable rhythms in compression-affected ECG segments, 90.9% for pulse-generating versus non-pulse-generating rhythms and 85.7% for pulse-generating rhythms versus non-pulse-generating rhythms in compression-affected ECG segments. The superior performance of the 1D-CNN, maintained in compression-affected ECG segments, highlights its potential for cardiac arrest rhythm classification during ongoing CPR. This capability could address a significant challenge in current resuscitation practices by potentially reducing interruptions in chest compressions for rhythm assessment. The superior performance of the 1D-CNN over the RNN in analyzing ECG segments from cardiac arrest patients can be attributed to several factors. The architectural characteristics of the 1D-CNN are particularly well suited for ECG signal analysis [19; 32; 33; 34; 35; 36]. Convolutional layers excel at capturing local patterns, which are crucial for rhythm classification [19; 32]. Pooling layers contribute to noise reduction and feature abstraction, which is advantageous when dealing with noisy ECG signals during resuscitation [33; 34]. Furthermore, the 1D-CNN typically requires fewer parameters due to its weight-sharing mechanism, leading to better generalization and reduced overfitting, especially when dealing with limited datasets [32; 35]. This parameter efficiency allows the 1D-CNN to effectively capture crucial local patterns in ECG signals. The ability of the 1D-CNN to maintain high performance even with compression-affected ECG data underscores its  
Figure 4: RNN vs. 1D-CNN accuracy for pulse-generating rhythm vs. non-pulse-generating rhythm. **(A)** Performance in all ECG segments. **(B)** Performance in compression-affected ECG segments. ECG—electrocardiogram; RNN—recurrent neural network; CNN—convolutional neural network. Figure 3: RNN vs. 1D-CNN accuracy for shockable vs. non-shockable rhythms. **(A)** Performance in all ECG segments. **(B)** Performance in compression-affected ECG segments. ECG—electrocardiogram; RNN—recurrent neural network; CNN—convolutional neural network. robustness and suitability for this critical medical application, potentially enabling more continuous and accurate rhythm analysis during resuscitation efforts. This advantage of the 1D-CNN over various artificial intelligence models was confirmed with a mini-review of 18 artificial intelligence studies on CPR-ECG [36]. These insights add to the growing body of research on deep learning applications for cardiac rhythm interpretation [9; 10; 11; 12; 13; 14; 15]. While our results demonstrate the potential of 1D-CNN models for classifying cardiac rhythms in CPR-affected ECG segments under controlled research conditions, significant development would be required before clinical implementation. Current performance metrics do not yet meet established AHA standards for rhythm detection, particularly for shockable rhythms. Further multi-center validation with larger, more diverse datasets would be essential to establish generalizability and clinical utility. Nevertheless, our study addresses the challenging scenario of cardiac arrest rhythm classification during ongoing CPR, including both manual and mechanical compressions, and goes a step further by tackling the additional challenge of pulse-generating rhythm detection during CPR, which is crucial for guiding resuscitation efforts.