## 4 Discussion  
In this research, we explore the option of merging two databases to evaluate the task of arrhythmia classification. For the task of arrhythmia classification, two algorithms were evaluated and compared. The merged database helped to balance the categories and expanded the amount of data used to train and validate the models. Before choosing the databases to merge, individual tests were performed with each database (cinc17, TNMG, MIT-BIH) to train the CNN model. The MIT-BIH database was excluded as it presented different characterization in annotations, leads, and signals compared with the cinc17 and TNMG databases. The merged database has not been used previously in any study and can serve as a benchmark for future studies that require access to a large ECG database. The merged dataset intended to design a balanced dataset containing two rhythm categories, normal sinus and abnormal signals, from differently acquired ECG signals (e.g., different acquisition devices, different sampling rates). Before training the CNN algorithm, the ECG signals were re-sampled to a common sample frequency and normalized. The arrhythmias classes reached high precision and accuracy of 90.46% and 93.07% for the normal sinus and 95.61% and 91.15% for the abnormal signals, respectively; proof that different databases can be merged for heart rhythm classification tasks under the same characteristics. By merging databases, researchers with no access to large ECG datasets can evaluate their models with wider data. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Author** & **Dataset** & **Classifier** & **Features** & **Accuracy** & **Precision** & **F1-Score** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Current Research \\ Study \\ ([PERSON] \\ et al.) \\ \end{tabular} } & \multirow{4}{*}{Merged dataset} & \multirow{4}{*}{\
\begin{tabular}{c} 1D-CNN [5] \\ (Wavelet \\ Transform + RR \\ Interval) \\ \end{tabular} } & \multirow{4}{*}{
\begin{tabular}{c} Deep Features \\ (Wavelet \\ Transform + RR \\ Interval) \\ \end{tabular} } & \multirow{4}{*}{93.07\%} & \multirow{4}{*}{93.04\%} & \multirow{4}{*}{93.05\%} \\  & & & & & \\ \cline{1-1} \cline{5-5} \cline{7-7}  & & SVM & & & & \\ \cline{1-1} \cline{5-5} \cline{7-7}  & & & & & \\ \cline{1-1} \cline{5-5} \cline{7-7}  & & & & & & \\ \cline{1-1} \cline{5-5} \cline{7-7}  & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of our study compared with previous studies based on state-of-the-art algorithms. It should be noted that each study used a different dataset, and it is the first time that the merged dataset is being used to classify cardiac arrhythmias. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classifier + Feature** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline
34 Layer CNN & 93.07\% & 93.04\% & 93.20\% & 93.05\% \\ \hline \begin{tabular}{c} SVM + Wavelet Transform \\ + RR (\(\gamma\) = 10; C = 10\(\times\) 10\({}^{7}\)) \\ \end{tabular} & 92.00\% & 89.04\% & 94.20\% & 91.54\% \\ \hline \begin{tabular}{c} SVM + Wavelet Transform \\ (\(\gamma\) = 10; C = 100) \\ \end{tabular} & 85.33\% & 86.15\% & 81.16\% & 83.58\% \\ \hline \begin{tabular}{c} SVM + Wavelet Transform \\ + (Wavelet Decomposition \\ + PSD) (\(\gamma\) = 10; C = 10\(\times\) 10\({}^{7}\)) \\ \end{tabular} & 85.33\% & 86.15\% & 81.15\% & 83.58\% \\ \hline \begin{tabular}{c} SVM + RR \\ (\(\gamma\) = 60; C = 1000) \\ \end{tabular} & 82.67\% & 82.09\% & 79.71\% & 80.88\% \\ \hline \
\begin{tabular}{c} SVM + Wavelet + HOS \\ (\(\gamma\) = 10; C = 10\(\times\) 10\({}^{7}\)) \\ \end{tabular} & 85.33\% & 83.0\% & 86.0\% & 84.0\% \\ \hline
\begin{tabular}{c} SVM + HOS \\ (\(\gamma\) = 0.1; C = 10) \\ \end{tabular} & 78.67\% & 78.46\% & 73.91\% & 76.12\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Overview of the results from the CNN algorithm and SVM + handcrafted feature extraction methods. Although the results with the merged database were good, during the pre-evaluation phase of the model, we noticed limitations in the algorithm.