## 5 Conclusions  
The development of computerized Electrocardiography (ECG) systems has increased the possibility of collecting more ECG data at the clinic or remotely [34]. This produces a large amount of patient data that needs to be reviewed by a cardiologist, which can consume a significant amount of time. Although ECG signal processing tools are already widely available to support this process, their development requires active human-assisted engineering for feature extraction. Furthermore, it will be interesting to explore whether systems based on ECG classification could work in parallel with cardiovascular disease prediction systems based on data mining. Previous studies have shown positive results in predictingcardiovascular disease using clinical data collected during medical examinations [35; 36]. In the case of a medical emergency, these systems could react quickly and autonomously alert nearby hospital alert systems or public safety answering points [37]. Implementing such applications in reliable mobile monitoring systems would benefit patients with limited access to continuous care.  
This exploratory study evaluates and compares the performance of two algorithms, the SVM and 1D-CNN model, for the task of rhythm classification by using a merged public dataset. SVM and 1D-CNN algorithms showed positive results with the merged database, showing similar F1-scores, precisions, and recall during arrhythmia classification. Despite the favorable results for both of them, it should be considered that in SVM, feature selection is a time-consuming trial-and-error process; meanwhile, CNN algorithms can reduce the workload significantly. The disadvantage of CNN algorithms is that it has higher computational processing cost; moreover, in the absence of access to powerful computational processing, SVM can be a reliable solution. It is important to note that when evaluating a learning algorithm, all the datasets must have the same characteristics, e.g., be re-sampled at the same sampling rate, and normalized.  
Conceptualization, L.M.; methodology, L.M.; writing--original draft preparation, L.M.; writing--review and editing, M.A., A.F. and J.M.M.; supervision, A.F.; funding acquisition, J.M.M. All authors have read and agreed to the published version of the manuscript. This work has been supported by \"FCT-Fundacao para a Ciencia e Tecnologia\" within the R&D Units Project Scope: UIDB/00319/2020.  
Not applicable.  
The data that support the findings of this study are openly available in Automatic ECG diagnosis using a deep neural network at doi:10.5281/zenodo.3625006 [10] and The PhysioNet/Computing in Cardiology Challenge 2017 at [[https://physionet.org/content/challenge-2017/](https://physionet.org/content/challenge-2017/)]([https://physionet.org/content/challenge-2017/](https://physionet.org/content/challenge-2017/)) (accessed on 15 October 2020) [8].  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ML & Machine Learning \\ DL & Deep Learning \\ DNN & Deep Neural Network \\ CNN & Convolutional Neural Network \\ MIT-BIH & The Massachusetts Institute of Technology Beth Israel Hospital Arrhythmia Database \\ CINC17 & PhysioNet Computing in Cardiology Challenge 2017 \\ TNMG & Telehealth Network of Minas Gerais (Brasil) \\ \end{tabular}