## 5 Experimental Results and Simulations
### Evaluation of Deep Learning Approaches  
Undiagnosed and untreated OSA is one of the main health burdens in the USA. OSA has many consequences that can affect a person's human life because it leads to several \begin{table}
\begin{tabular}{l c c} \hline \hline
**Measure** & **Testing Results** & **Validation Results** \\ \hline Accuracy & 74.47\% & 78.95\% \\ TPR & 79.99\% & 76.20\% \\ TNR & 62.60\% & 84.00\% \\ AUC & 71.29\% & 80.10\% \\ precision & 82.16\% & 89.76\% \\ fscore & 81.06\% & 82.42\% \\ G-mean & 70.76\% & 80.01\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Results of ensemble DT* through testing and validation. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**\#Features** & **Accuracy** & **TPR** & **TNR** & **AUC** & **Precision** & **Fscore** & **G-Mean** \\ \hline
1 & 60.72\% & 72.17\% & 36.08\% & 54.12\% & 70.86\% & 71.51\% & 51.02\% \\
2 & 73.95\% & **81.36\%** & 57.98\% & 69.67\% & 80.66\% & 81.01\% & 68.68\% \\
3 & 73.00\% & 77.72\% & 62.83\% & 70.28\% & 81.83\% & 79.73\% & 69.88\% \\
4 & 72.93\% & 77.78\% & 62.47\% & 70.13\% & 81.70\% & 79.69\% & 69.71\% \\
5 & 73.12\% & 77.81\% & **63.00\%** & 70.41\% & 81.92\% & 79.81\% & 70.02\% \\
6 & 73.31\% & 78.27\% & 62.62\% & 70.44\% & 81.85\% & 80.02\% & 70.01\% \\
7 & 74.50\% & 80.07\% & 62.51\% & 71.29\% & 82.14\% & 81.09\% & 70.75\% \\
8 & **74.56\%** & 80.01\% & 62.83\% & **71.42\%** & **82.26\%** & **81.12\%** & **70.90\%** \\
9 & 74.47\% & 79.99\% & 62.60\% & 71.29\% & 82.16\% & 81.06\% & 70.76\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Results of Relieff filter-based FS with incremental number of features based on their importance. serious health problems, such as heart attacks, stroke, increased possibility of traffic accidents, and sudden death. Polysomnography (PSG) is considered the gold way for the exact diagnosis of OSA that needs a patient to spend a night at a sleep center. The analysis of the data collected is normally implemented by a practitioner who oversees studying hours of ECG records. However, this method is not fully accurate and hectic. Recently DL was proposed as a method to handle this task. Several types of DL models can be used to diagnose sleep apnea, such as Recurrent Neural Networks (RNN), Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM). DL models can model complex nonlinear systems with high classification accuracy. A CNN consists of three main components: convolution layer, pooling layer, and classification layer. In the convolution layer, the feature map is obtained by utilizing a filter kernel to generate the convolution integral of the input data. In the pooling layer, the feature map is reduced and confined to the dimensions of input data. Finally, the classification layer uses a fully connected network to accomplish the classification task (See Figure 10). Deep Neural Networks was successfully used for sleep apnea-hypopnea severity classification in [47]. A deep neural network system with four hidden layers was developed utilizing a feature normalization technique called Covariance Normalization (CVN) in [48; 49]. In this work, Four DL models were evaluated: RNN, one-dimensional CNN, LSTM, and a hybrid model of CNN and LSTM (CNNLSTM) introduced by [PERSON] and [PERSON] [50]. The detailed parameters settings of these models are presented in Table 11 based on the recommendation of study in [50].