## 5 Experimental Results and Simulations
### Evaluation of Deep Learning Approaches
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **RNN** & **CNN** & **LSTM** & **CNNLSTM** \\ \hline No. layers & 1 & 1,2 & 1 & 1,2 \\ No. units & - & 512,256 & - & 512,256 \\ Activation function & ReLU & ReLU & ReLU & ReLU \\ Loss function & categorical, crossentropy & categorical, crossentropy & categorical, crossentropy \\ epochs & 250 & 250 & 250 & 250 \\ optimizer & SGD, Adam & SGD, Adam & SGD, Adam & SGD, Adam \\ Learning rate (SGD) & \(1\times 10^{-3}\) & \(1\times 10^{-3}\) & \(1\times 10^{-3}\) & \(1\times 10^{-3}\) \\ decay (SGD) & \(1\times 10^{-5}\) & \(1\times 10^{-5}\) & \(1\times 10^{-5}\) & \(1\times 10^{-5}\) \\ Momentum(SGD) & 0.3 & 0.3 & 0.3 & 0.3 \\ No. fully connected layers (Dense) & 1, 2 & 1, 2 & 1, 2 & 1, 2 \\ No. fully connected units & 2048, 1024 & 2048, 1024 & 2048, 1024 & 2048, 1024 \\ No. LSTM units & - & - & 512 & 512 \\ No. RNN units & 512 & - & - & - \\ Dropout & 0.25 & 0.25 & 0.15 & 0.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Parameters of deep learning models. Figure 10: An example of a three layers CNN model. Table 12 shows the classification performance of four deep learning models using two different optimizers (SGD and Adam). As can be observed, RNN worked better when implementing the SGD, with the accuracy and AUC of 0.8050 and 0.8837. As for CNN, LSTM, and CNNLSTM, we can see that these models achieved the optimal performance using Adam. Among these deep learning methods, the CNNLSTM has contributed the highest accuracy (0.9075), precision (0.9148), F1-score (0.9163), and AUC (0.9746). From the analysis, it is clear that the CNNLSTM can usually offer an accurate diagnosis of OSA. Ultimately, the CNNLSTM is the best deep learning method in the evaluation process, and hence only CNNLSTM will be applied in the rest of the analysis. Table 13 presents the training and validation results of the CNNLSTM. Based on the result obtained, the CNNLSTM was able to retain a high accuracy (0.8625) and AUC (0.9510) in the validation stage, which gives a better and accurate diagnosis of the OSA. Furthermore, we compare the performance of our CNNLSTM to other models in the literature. Table 14 outlines the performance comparison of the proposed CNNLSTM model with other seven studies. Among the previous studies, three of them were applying the machine learning while four of them were implementing the deep learning models. In Table 14, the highest accuracy of 0.8790 is obtained by 1-D CNN approach, and our CNNLSTM is ranked at third. In terms of the AUC measurement, it is obvious that our CNNLSTM has achieved the best result, 0.9510 compared to other studies. Ultimately, the proposed CNNLSTM can be considered as a valuable tool in diagnosing the OSA. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Dataset** & **Accuracy** & **Recall** & **Precision** & **F1-Score** & **AUC** \\ \hline \multirow{2}{*}{CNNLSTM} & Training & 0.90750 & 0.91919 & 0.91476 & 0.91627 & 0.97462 \\  & Validation & 0.86250 & 0.88794 & 0.86855 & 0.87682 & 0.95103 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Results of best-performing classifier CNNLSTM for the training and validation datasets \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Study** & **Year** & **Technique** & **Classifier** & **Accuracy** & **Recall** & **AUC** \\ \hline [PERSON] et al. [28] & 2015 & ML & LS-SVM & 0.8474 & 0.8471 & 0.8807 \\ [PERSON] et al. [51] & 2016 & ML & SVM-HMM & 0.8620 & 0.8260 & 0.9400 \\ [PERSON] and [PERSON] [25] & 2016 & ML & LS-SVM & 0.8380 & 0.7950 & 0.8300 \\ [PERSON] et al. [29] & 2018 & DL & Decision Fusion & 0.8470 & 0.8890 & 0.8690 \\ [PERSON] and [PERSON] [52] & 2019 & DL & AlexNet CNN + Decision Fusion & 0.8620 & **0.900** & 0.8800 \\ [PERSON] et al. [26] & 2019 & DL & LeNet-5 CNN & 0.8760 & 0.8310 & 0.9500 \\ [PERSON] et al. [30] & 2020 & DL & 1-D CNN & **0.8790** & 0.8110 & 0.9350 \\ Our approach & & DL & CNNLSTM & 0.86250 & 0.88794 & **0.95103** \\ \hline \hline \end{tabular}
\end{table}