###### Abstract  
The success of arrhythmia classification tasks with Machine Learning (ML) algorithms is based on the handcrafting extraction of features from Electrocardiography (ECG) signals. However, feature extraction is a time-consuming trial-and-error approach. Deep Neural Network (DNN) algorithms bypass the process of handcrafting feature extraction since the algorithm extracts the features automatically in their hidden layers. However, it is important to have access to a balanced dataset for algorithm training. In this exploratory research study, we will compare the evaluation metrics among Convolutional Neural Networks (1D-CNN) and Support Vector Machines (SVM) using a dataset based on the merged public ECG signals database TNMG and CINC17 databases. Results: Both algorithms showed good performance using the new, merged ECG database. For evaluation metrics, the 1D-CNN algorithm has a precision of 93.04%, an accuracy of 93.07%, a recall of 93.20%, and an F1-score of 93.05%. The SVM classifier (\(\lambda=10\), \(C=10\times 10^{9}\)) achieved the best classification metrics with two combined, handcrafted feature extraction methods: Wavelet transforms and R-peak Interval features, which achieved an overall precision of 89.04%, accuracy of 92.00%, recall of 94.20%, and F1-score of 91.54%. As an unique input feature and SVM (\(\lambda=10\), \(C=100\)), wavelet transforms achieved precision, accuracy, recall, and F1-score metrics of 86.15%, 85.33%, 81.16%, and 83.58%. Conclusion: Researchers face a challenge in finding a broad dataset to evaluate ML models. One way to solve this problem, especially for deep learning models, is to combine several public datasets to increase the amount of data. The SVM and 1D-CNN algorithms showed positive results with the merge of databases, showing similar F1-score, precision, and recall during arrhythmia classification. Despite the favorable results for both of them, it should be considered that in the 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 a higher computational processing cost; moreover, in the absence of access to powerful computational processing, the SVM can be a reliable solution. heart arrhythmia; convolutional neural network; support vector machines; handcrafted features; deep features +
Footnote †: journal: Applied sciences  
extracted to assess the patient's overall cardiac health [1; 2]. The characteristics of an ECG signal should be considered, including heart rate, pulse origin, pathway, and propagation velocity. For instance, if an electrical impulse does not propagate through the heart's normal conduction pathway, the ECG's original morphology can be significantly distorted, leading to arrhythmia [3]. Some cardiac-related pathologies can be detected from short-term data acquisition in the hospital. In contrast, others require long-term monitoring through medical devices such as the Holter monitor and, more recently, wearables, resulting in large amounts of data that must be analyzed and processed. Automatic ECG Signal analysis systems have a crucial role in assisting healthcare professionals by providing real-time alarms for immediate treatment in intensive care units (ICUs) and improving people's quality of life through the early detection of abnormal patterns [2]. The system is based on signal processing and artificial intelligence algorithms. Artificial Intelligence is an extensive field segmented into multiple divisions, one of them being Machine Learning (ML), which has a sub-vision called Deep Learning (DL). Both divisions are broad and have experienced increasing popularity both in and outside the medical field over the last years [2; 4]. As the access to more processing resources has increased over the recent decades, the number of studies and development of ML algorithms for cardiac arrhythmia classification tasks have grown.