## 3 Results  
The descriptive statistics of age among the 131 participants were 28 (min), 61 (Q1), 71 (median), 79 (Q3) and 99 (max). The proportions of men and women were 85 (64.9%) and 46 (35.1%), respectively. Among the total 508 ECG recordings, including both compression and non-compression phases, the distribution was as follows: asystole--104 (20.5%); PEA--164 (32.3%); VF--104 (20.5%); VT--20 (3.9%); and ROSC rhythm--116 (20.9%). Table 1 and Figures 3 and 4 present the performance outcomes of the 1D-CNN and the RNN for oversampling data during 100 epochs. Here, oversampling denotes that the observations of shockable (or pulse-generating) rhythms were doubled for the detection of shockable (or pulse-generating) rhythms, resolving class imbalance problems. The 1D-CNN registered comparable or better test accuracy results compared to the RNN across the board (Table 1): 91.3 versus 50.6 for the detection of shockable rhythms in all ECG segments; 89.8 versus 54.4 for the detection of shockable rhythms in compression-affected ECG segments; 90.9 versus 92.2 for the detection of pulse-generating rhythms in all ECG segments; and 85.7 versus 84.4 for the detection of pulse-generating rhythms in compression-affected ECG segments. The results were similar in terms of test F1-scores, harmonic means of test sensitivity and test specificity (Table 1): 90.8 versus 34.3 for the detection of shockable rhythms in rhythms in all ECG segments; 89.6 versus 50.7 for the detection of shockable rhythms in compression-affected ECG segments; 89.1 versus 91.8 for the detection of pulse-generating rhythms in all ECG segments; and 85.1 versus 82.9 for the detection of pulse-generating rhythms in compression-affected ECG segments. In Figure 3 (shockable vs. non-shockable rhythm) and Figure 4 (pulse-generating vs. non-pulse-generating rhythm), the validation-set accuracy of the 1D CNN rises during initial 25 epochs, and then it stabilizes during the remaining 75 epochs. A similar result can be observed for the RNN in Figure 4.  
\begin{table}
\begin{tabular}{l l l l l} \hline \multicolumn{5}{l}{**Shockable Rhythms vs. Non-Shockable Rhythms**} \\ \hline  & **Sensitivity** & **Specificity** & **F1-Score** & **Accuracy** \\ \hline
**CNN** & 98.0 & 84.5 & 90.8 & 91.3 \\ \hline
**RNN** & 22.2 & 75.8 & 34.3 & 50.6 \\ \hline \multicolumn{5}{l}{_Performance in compression-affected ECG segments_} \\ \hline
**CNN** & 88.6 & 90.7 & 89.6 & 89.8 \\ \hline
**RNN** & 40.9 & 66.7 & 50.7 & 54.4 \\ \hline \multicolumn{5}{l}{**Pulse-Generating Rhythms vs. Non-Pulse-Generating Rhythms**} \\ \hline  & **Specificity** & **Sensitivity** & **F1-Score** & **Accuracy** \\ \hline
**CNN** & 100 & 80.3 & 89.1 & 90.9 \\ \hline
**RNN** & 97.4 & 86.8 & 91.8 & 92.2 \\ \hline \multicolumn{5}{l}{_Performance in compression-affected ECG segments_} \\ \hline
**CNN** & 90.2 & 80.6 & 85.1 & 85.7 \\ \hline
**RNN** & 92.7 & 75.0 & 82.9 & 84.4 \\ \hline \end{tabular} Note: all values are presented as percentages. CNN—convolutional neural network; RNN—recurrent neural network; ECG—electrocardiogram. Sensitivity is the proportion of true positives among real positives, whereas specificity is the proportion of true negatives among real negatives. F1-score is the harmonic mean of sensitivity and specificity. Accuracy is the proportion of correct predictions (both true positives and true negatives) among all cases. Performance in compression-affected ECG segments refers to the model’s ability to classify cardiac arrest rhythms in the chest compression phase.  
\end{table}
Table 1: Model performance: 1D-CNN vs. RNN after 100 epochs.