## 3 Results  
After pre-processing and normalization, the SVM classifier and 1D-CNN model were trained and validated with the merged dataset. For each experiment, multiple tests and setups were realized to observe the most suitable handcrafted features for the task of ECG classification with the SVM classifier. From all setups, better results will be reported. Overall, the SVM and 1D-CNN algorithms adjusted well to the merged dataset. The performance metrics for the 1D-CNN model with the merged dataset are presented in Table 2 and the confusion matrix in Figure 6. The results are by individual arrhythmia classification, where for normal rhythms (N), a precision of 90.46%, recall 95.24%, accuracy 93.07%, and F1-Score 92.79% were obtained; and other rhythms with a precision of 95.46%, recall 91.15%, accuracy 93.07%, and F1-Score 93.32%. The SVM classifier presents two types of results, first with single handcrafted feature extraction methods using 1500 signals of the merge database. The results show that the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classification** & **Precision** & **Recall** & **Accuracy** & **F1-Score** & **Support** \\ \hline N & 90.46\% & 95.24\% & 93.07\% & 92.79\% & 355 \\ O & 95.61\% & 91.15\% & 93.07\% & 93.32\% & 365 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Scores results report for the 1D-CNN model. Figure 6: CNN Model Confusion Matrix. use of the wavelet transform proposed by [PERSON] et al. achieved the highest accuracy, precision, recall, and F1-score metrics of 85.33%. 86.15%, 81.16%, and 83.58% respectively. The RR features achieved the second-best results with 82.67% maximum accuracy, 82.09% precision, 79.71% recall, and 80.88% F1-score. The Uniform Local Binary Pattern (ULBP) met the lowest overall highest performance feature, making out at only 58.67% accuracy, 53.68% precision, 73.91% recall, and 62.20% F1. The results for single features used to train the SVM classifier are reported in Table 3. Second, to explore the possibilities of improving the accuracy of the SVM using handcrafted feature extraction, new training iterations were conducted under the same conditions with two combined features instead of a single feature, assuming that more features lead to better classification performance. Given that the single feature, wavelet transform, provided the best maximum accuracy, this feature has been paired with the other features for this task. The Wavelet Transform-based feature was selected as the base feature, i.e., the first of the two provided features, with the second feature iterating over the remaining available features. The goal was to analyze the impact of combining multiple features on the classification task. Eventually, the following pairs were provided: WT + HOS, WT + ULBP, WT + R-peak interval features, and WT + wavelet decomposition. The results from combined features are shown in Table 4. The values in parenthesis represent the percentage difference from the single feature results. The results showed a minimum accuracy for each combination of wavelet transform and second feature by around 46.00%. The former highest accuracy of 85.33% by the wavelet transform alone has now been surpassed by the combination of wavelet transform and RR features, resulting in a 92.00% accuracy. No changes in accuracy have resulted when combining the wavelet transform with the HOS feature, and an accuracy drop of 16.67% is observed when combining the wavelet with the ULBP feature. The combination of wavelet transform and wavelet decomposition with PSD resulted in only a minor increase in accuracy, which is still not significant.