## 4 Discussion  
The nonlinear processing in the stacked of multiple layers is well suited to capturing highly varied functions with the conciseness of the parameters set. Based on unsupervised pre-training, DL allows assigning deeper networks in a parameter space region to avoid local minima. The availability of large sets or even with only a small number of data, DL techniques achieves excellent performance, and often the best one. Due to only a few studies using DL in arrhythmia classification, several experiments were designed to benchmark our proposed model. We have selected several ML methods to benchmark our model, and the result is compared in terms of accuracy, sensitivity, specificity, precision, and F1-score (see Tables 8 and 9) [34, 35, 36]. From Table 8, shallow architecture like SVM and DNNs produce a good performance, but from all results, a DNNs model with PCA and DWT produces high performance, such as accuracy of about 99.76%, precision of about 98.20%, sensitivity of about 91.80%, specificity of about 99.78% and an F1-measure of about 97.80%. However, feature extraction in shallow architecture is very difficult; it must be processed separately and manually. On the other hand, DL has a feature learning approach. It learns the feature directly from the network by using its structure. Such an approach can be used as both dimensionality reduction and noise cancelation. It reduces the time process and gives a simpler procedure with a big and imbalanced data set. As a result found that DL performances are lower compared with the SVM and DNNs counterpart, in terms of precision about 93.60% and F1-Measure about 91.80% (DL with DAEs). However, the advantage of DL is that it does not require a human to identify and compute the  
Figure 6: Classifier performances. critical features. DL learns discriminatory features that best predict the outcomes. This prediction means that the amount of human effort required to train DL systems is less (because no feature engineering or computation is required) and may also lead to the discovery of important new features that were not anticipated. In addition, due to its ability to learn critical feature directly from data, even with highly imbalanced data, the learned features are still robust enough to discriminate the class labels. Moreover, from Table 9, our proposed architecture is compared with other DL performances. However, many previous studies calculate only 3 metrics without the F1 Score and precision, whereas in the medical data, especially in Arrhythmia, the data set is imbalanced. Therefore, the complete metrics to measure the prediction must be calculated. It can be shown that our proposed DL model based on DAEs and DNNs are leading with others DL like CNN, DBN, and RNN. The DL produces accuracy of about 99.73%, sensitivity of about 91.20%, specificity of about 99.80%, precision of about 93.60%, and an F1-Score of about 91.80%. In addition, to analyze memory usage and the speed of processing time, a comparison between DL architecture and Shallow Architecture was conducted. Figures 7 and 8 illustrate that the shallow architecture with an SVM classifier produces fast processing time and less memory usage. However, by using shallow architecture, the process of feature extraction and feature reduction was manually engineered without considering the low-level feature from the data. This requires more time and effort to investigate the stage. Thus, only the best value is selected to obtain excellent performance. In this study, the comparison three classifiers i.e., DNNs, SVM, and DL are presented. According to the experimental result, it is found that the SVM classifier with PCA and DWT produce the best result that what, as presented in Table 8. Unfortunately, when a large data set is used, the SVM performance is decreased. This problem occurred because the SVM classifier is cost-inefficient when an extensive data set is applied. This was mostly a small data set problem.