## 3 Experimental Result
### Results
In this study, DAEs reduced the feature from 252 into 32 features while PCA with a cumulative energy value of 0.99, the initial feature is lowered to 32 features for 10 (ten) classes. It can be seen in Figure 4b,c that there is a significant difference between the DAEs and PCA in terms of data distribution plots. By using PCA, it is assumed that the data lie on or near a linear subspace of the high-dimensional space. However, DAEs do not rely on the linearity assumption as a result of which more complex embedding of the data in the high-dimensional space. Therefore, the data distribution from PCA remains unstructured, and it is centered at a certain point, hence it is challenging to discriminate data patterns from each class. DAEs, on the other hand, produces a better data distribution. Its feature distribution becomes clearer so that the minority classes are not polarized with larger classes. Figure 4: Data distribution plots in three conditions; raw data, PCA, and DAEs. Moreover, DAEs was able to eliminate spikes from the original signal without losing the information in the ECG signal and was also able to learn nonlinear feature representations, as presented in Figure 4. All colors in Figure 4a-c respectively, show 10 (ten) classes of Arrhythmia such as N, A, V, R, L, P,!, F, f, and j. To obtain an optimum classification performance, various DL structures in the learning process were examined. Ten structures were determined and validated prior to the selection of the best model. All classifiers were arranged in two processes: training and testing. The processing time was investigated in our model because the structure must embeddable in the hardware as an ECG interpretation module for further application. All processing time results of 10 DL structures are presented in Table 2.  
From Table 2, the experiment describes training time for feature learning, classifier, and testing time. In this case, the trade-off happened between model complexity and processing time. It worth noting that the processing time increases with a more complex DL structure. However, a smaller architecture is better for feasible application on the real-time system. Hence, we selected the DAEs architecture with 5 layers combined with DNNs with 3 hidden layers (model 8). It produces a good performance in terms of accuracy, sensitivity, specificity, precision, and F1-score compare to the other structures. As presented in Table 2, a 10 structures model validation of DNNs with a DAEs feature learning model are selected for further analysis. Such a process was done by changing the number of neurons and the number of hidden layers, activation functions, and cost functions of DAEs and DNNs structures. The training process uses 80% of the data, and the testing process uses the remaining data percentage. The validation phase for all models is presented in Table 4, and Table 5, respectively. Table 4 shows the results from the various DL structures in the training process. Based on validation model 8 in Table 3, the training and testing process produces the best performance metrics to all classes (see Tables 4 and 5). The training process produces 99.9% accuracy, 94.1% sensitivity, 99.9% specificity, 97.4% precision and a 95.7% F1-Score, while in the testing process produce 99.7% accuracy, 91.2% sensitivity, 99.6% specificity, 93.6% precision and a 91.8% F1-Score. For a metric scheme, the values of sensitivity and specificity will change along with the cutoff value. In every cutoff value, a dot can be plotted using the coordinate (Specificity, Sensitivity). A curve that connects all these dots is called a Receiver Operating Characteristics (ROC) curve. The ROC curve which is far away above the diagonal, especially in the upper-left corner, produces the right predictions with random guesses. However, if the dataset is highly imbalanced, the shape of the ROC curve is biased and may be misleading [32, 33]. The MIT BIH arrhythmia dataset is imbalanced, due to the higher number of normal figures compared to disorderly ones.