## 4 Discussion
During training, we encountered overfitting during the pre-evaluation of the model for heartbeat classification. For this reason, we suggest that the algorithm's performance be evaluated and compared with less complex CNN algorithms. Another limitation we faced was that the model is designed to perform a classification every 256 samples, which means that the ECG signals must have a specific frequency range that allows the signal to be a multiple of 256; otherwise, the algorithm shows errors during training. Moreover, the algorithm overperforms with relatively small datasets, which leads to a longer training cycle to stabilize the evaluation loss in comparison with the performance of the model in larger datasets. Human-assisted feature extraction and feature engineering have been conducted for the SVM classifier. The results show that the Wavelet Transform-based feature provides the overall highest metrics (accuracy: 85.33%, precision: 86.15%, recall: 81.16%, F1-score: 83.58%). The remaining features, listed in descending order by the maximum achieved accuracy, were the RR-interval feature, HOS, wavelet decomposition, and the ULBP. To these results, the maximum accuracy provided by the individual features alone is inferior to the performance of the CNN. In order to further explore the possibilities of improving the maximum accuracy of the human-assisted feature engineering for SVM, an additional experiment has been conducted. Instead of providing a single feature type to the SVM, a combination of two features was provided to the SVM for the classification task. The results showed that the combinations of WT + WT R-peak intervals provided the highest increase in accuracy, reaching a maximum accuracy of 92.00%. At the same time, the remaining features did not strongly influence the WT's original accuracy or led to a decrease in accuracy by up to 16.67%, which was the case of combined featured WT + ULBP. When comparing the complexity of the features by the amount of information provided to the SVM per feature, it can be observed that the use of the WT benefits from combinations with features that provide lower amounts of features. For instance, the R-peak interval features provide four feature values, while the worst score combination of WT + ULBP adds 59 values to the feature set. It can be assumed that the combinations with lower amounts of data generated a less complex pattern for the correct classification than the features with high data amounts that create a complex pattern. The results show that selecting suitable features must be carefully made depending on the application, among other factors that might impact the outcome. Several studies reported results based on the overall accuracy. However, overall accuracy as the only performance metric is not enough to assess the performance of a model in a classification task. For this reason, additional tools and metrics such as the confusion matrix show the proportion of true prediction results for each heart rhythm. Human-assisted feature engineering requires the application of the signal processing techniques directly on the ECG input signal is rather beneficial as it does not require support for the high computational costs of running the CNN network to extract features before classification. A CNN algorithm's advantage is providing more complex features, potentially even revealing early indicators of diseases that might not be revealed from traditional signal processing of raw ECG input signals.