## 3 Experimental Result
### Results  
Figure 4a-c illustrates the data distribution among the classes. It can be seen that the initial ECG data distribution, which consists of 252 features representing a raw ECG signal, was unstructured and imbalanced; therefore, it can influence one another. In order to minimize the complexity of the data by reducing data dimensionality, PCA and DAEs are used and compared. PCA is one of the main linear dimensionality reduction techniques for extracting effective features from high dimensions' data, while DAEs is nonlinear techniques. The nonlinear techniques have an advantage over linear techniques for solving the problems of the real-world because real-world data are nonlinear in nature like medical data. From the previous research, it is observed that the nonlinear techniques are performing better than linear techniques on artificial tasks and succeed in overcoming poor natural datasets [25; 27]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Feature Learning** & **DAEs** & **Classifier** & **DNNs** & **Process-Sing** & **DNNs Testing** \\  & **Learning** & **Training Time (s)** & **Structure** & **Training Time (s)** & **Time (s)** & **Time (s)** \\ \hline \multirow{2}{*}{1} & Auto-Encoder & \multirow{2}{*}{991.07} & MLP & \multirow{2}{*}{243.46} & \multirow{2}{*}{1234.53} & \multirow{2}{*}{0.08} \\  & (252 - 32 - 252) & & & & \\ \hline \multirow{2}{*}{2} & Auto-Encoder & \multirow{2}{*}{991.07} & DNN 2 HLs & \multirow{2}{*}{280.94} & \multirow{2}{*}{1272.01} & \multirow{2}{*}{0.1} \\  & (252 - 32 - 252) & & & & \\ \hline \multirow{2}{*}{3} & Auto-Encoder & \multirow{2}{*}{991.07} & DNN 3 HLs & \multirow{2}{*}{313.33} & \multirow{2}{*}{1304.41} & \multirow{2}{*}{0.1} \\  & (252 - 32 - 252) & & & & \\ \hline \multirow{2}{*}{4} & Auto-Encoder & \multirow{2}{*}{991.07} & DNN 4 HLs & \multirow{2}{*}{327.84} & \multirow{2}{*}{1318.92} & \multirow{2}{*}{0.13} \\  & (252 - 32 - 252) & & & & \\ \hline \multirow{2}{*}{5} & Auto-Encoder & \multirow{2}{*}{991.07} & DNN 5 HLs & \multirow{2}{*}{354.68} & \multirow{2}{*}{1345.75} & \multirow{2}{*}{0.42} \\  & (252 - 32 - 252) & & & & \\ \hline \multirow{2}{*}{6} & Deep & \multirow{2}{*}{1866.67} & MLP & \multirow{2}{*}{241.24} & \multirow{2}{*}{2107.91} & \multirow{2}{*}{0.08} \\  & (252 - 128 - 64 - 32 - 252) & & & & \\ \hline \multirow{2}{*}{7} & Deep & \multirow{2}{*}{1866.67} & MLP 2 HLs & \multirow{2}{*}{273.55} & \multirow{2}{*}{2140.22} & \multirow{2}{*}{0.09} \\  & (252 - 128 - 64 - 32 - 64 - 128 - 252) & & & & \\ \hline \multirow{2}{*}{8} & Deep & \multirow{2}{*}{1866.67} & DNN 3 HLs & \multirow{2}{*}{315.34} & \multirow{2}{*}{2182.01} & \multirow{2}{*}{0.10} \\  & (252 - 128 - 64 - 32 - 10) & & & & \\ \hline \multirow{2}{*}{9} & Deep & \multirow{2}{*}{1866.67} & DNN 4 HLs & \multirow{2}{*}{353.26} & \multirow{2}{*}{2219.93} & \multirow{2}{*}{0.12} \\  & (252 - 128 - 64 - 32 - 10) & & & & \\ \hline \multirow{2}{*}{10} & Deep & \multirow{2}{*}{1866.67} & DNN 3 HLs & \multirow{2}{*}{381.51} & \multirow{2}{*}{2248.18} & \multirow{2}{*}{0.14} \\  & (252 - 128 - 64 - 32 - 64 - 32 - 64 - 128 - 252) & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Processing time of 10 DL structures. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Input Layer** & **Output Layer** & **Hidden Layer** & **Activation** & **Activation** & **Learning** & **Loss** & **Batch** \\  & **Layer** & **Layer** & **Neuron** & **Hidden** & **Output** & **Rate** & **Function** & **Size** \\ \hline DAEs & \multirow{2}{*}{252} & \multirow{2}{*}{252} & \multirow{2}{*}{128-64-32-64-128} & \multirow{2}{*}{ReLU} & Sigmoid & \multirow{2}{*}{0.0001} & MSE & 8 \\ Pre-Training & & & & & & & \\ DNNs & \multirow{2}{*}{32} & \multirow{2}{*}{10} & \multirow{2}{*}{100-50-50-50-100} & ReLU & Softmax & \multirow{2}{*}{0.001} & Cross-Entropy & 32 \\ Fine-Tuning & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The proposed structure of deep learning of model 8.