## 1 Introduction  
Out-of-hospital cardiac arrest (OHCA) is a leading cause of mortality worldwide, with an estimated global incidence of 55 cases per 100,000 person-years [1]. The early and accurate diagnosis of the underlying cardiac rhythm is crucial for improving survival rates and neurological outcomes in cardiac arrest patients [2, 3]. The initial heart rhythm determines the appropriate treatment strategy, such as defibrillation for shockable rhythms (ventricularfibrillation and pulseless ventricular tachycardia) or continued cardiopulmonary resuscitation (CPR) for non-shockable rhythms (asystole and pulseless electrical activity). Current methods for heart rhythm classification in cardiac arrest rely on the manual interpretation of electrocardiogram (ECG) signals by emergency medical services personnel or automated external defibrillators (AEDs). However, these approaches have significant limitations. Manual interpretation requires skilled personnel and is prone to human error [4], while existing automated algorithms often have suboptimal accuracy, especially in the presence of artifacts [5,6]. Moreover, the need to pause CPR for rhythm analysis can negatively impact patient outcomes, as chest compression interruptions lead to decreased cerebral and myocardial perfusion [7,8].  
Recent advances in deep learning have shown promising results in ECG analysis and arrhythmia detection [9, 10, 11, 12, 13, 14, 15]. One-dimensional convolutional neural networks (1D CNNs) and recurrent neural networks (RNNs) have demonstrated superior performance in noise detection and ECG classification compared to traditional machine learning methods [9, 11, 12, 13]. These algorithms can automatically learn hierarchical features from raw ECG signals and capture complex temporal patterns, enabling more accurate and efficient rhythm classification. In this context, some studies have explored the use of artificial intelligence for analyzing ECG rhythms during chest compressions to determine the need for defibrillation [16, 17, 18, 19, 20], whereas few studies have focused on pulse-generating rhythms during ongoing CPR [21]. However, the existing literature does not pay due attention to the following: (1) pulse-generating rhythms (indicating the return of spontaneous circulation, ROSC) can be detected early even during chest compressions; and (2) shockable rhythms (among non-pulse-generating rhythms) can be detected early regardless of chest compressions.  
This study aims to address these gaps by developing and evaluating deep learning models for the real-time classification of heart rhythms in cardiac arrest using ECG data. By comparing the performance of 1D-CNN and RNN models on both compression-affected and non-compression ECG data, we aim to assess the feasibility of developing robust algorithms capable of accurate rhythm classification during ongoing resuscitation efforts in terms of the following: (1) pulse-generating rhythms (indicating ROSC) vs. non-pulse-generating rhythms early, even during chest compressions; and (2) shockable rhythms (among non-pulse-generating rhythms) vs. non-shockable rhythms early regardless of chest compressions. The successful implementation of such models could potentially reduce unnecessary pauses in CPR, facilitate timely defibrillation when appropriate and ultimately improve outcomes for cardiac arrest patients.