## 5 Results and Discussion
### Results  
This section explains our experiment results in detail and presents our proposed model efficiency compared to other benchmark approaches and how selected hyperparameters influence our recognition models. The results show that the Bayesian optimization algorithm based on the Gaussian method can achieve higher accuracy in a few data samples based on the experiments we performed compared with manual and other search methods. The performance was assessed over 400 iterations, the initial batch size of 42 samples with early termination, and 12 patients, which is the number of iterations showing no improvement. Early stopping mode was set to \"auto\" to increase or decrease in validation accuracy automatically. Based on Bayesian optimization, we selected the top five models over 25 epochs. Several convolutional layers and their number of filters plays an important role in detecting high-level features. It is also noticed that increasing the number of convolution layers will significantly impact the accuracy of the model recognition with waveform input and the four convolution layer networks achieve the best performance. We noticed that increasing the number of stacked network layers under the same features map does not improve recognition accuracy. Convolutional filters search array are set to \(\{16,\cdots,512\}\) with a step size of 2. The network uses the strategy of repeated stacking. We used multiple optimizers to optimize the convex and non-convex optimization network, such as Adam, AdaBound, and EAG. We trained the networks with various initial learning rate values (0.1, 0.01, and 0.001), and we found that (0.001) performed better in all models. In order to find the optimum batch size, we compared the models with various batch sizes within the range of \([16,\cdots,256]\), and the batch size of 54 obtained the best results in conducted experiments. Finally, our models adopt 50 training epochs due to no further validation accuracy improvement. Table 4 summarizes the best configurations for the different models. The epoch's hyperparameters with the highest validation accuracy are determined as the final hyperparameters for each selected model. Bayesian optimization achieved good results after a few iterations, and no significant improvements were apparent in extra iterations. 1D CNN accuracy improved by 2% compare to default configurations. Performance on test set by the five models is summarized in Table 5 and the curves for validation set are displayed in Figures 2 and 3. As shown in training curves, the training was finished after 63 epochs due to no further validation accuracy improvement. Model loss decreased over time for both training and validation datasets, indicating that the model learned from the dataset as expected. Similarly, as learning efficiency improved, model training and validation accuracy increased over time. Model loss and accuracy variations over various epochs are due to hidden dropout layers. The gap between curves for training and validation implies that overfitting was minimal; dropout was used to prevent overfitting and improve computational efficiency. The maximum and minimum model loss and accuracy using the validation dataset were (0.353, 0.469) and (88.8%, 91.4%), respectively. The average accuracy of 25 iterations achieved by the 1D CNN on test set in selected models were 89.6%, 91.4%, 90.1%, 89.7%, and 90.0%, respectively, as shown in Table 6. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Filters** & **Kernels** & **Batch Size** & **Activation \(f\)** & **Optimizer** & **Learning Rate** & **Dropout Rate** \\ \hline
1 & 141 & 4 & 42 & ReLU & AdaBound & 0.007 & 0.266 \\
2 & 100 & 2 & 36 & — & Adam & 0.003 & 0.181 \\
3 & 98 & 2 & 44 & — & EAG & 0.005 & 0.258 \\
4 & 64 & 3 & 58 & — & — & 0.003 & 0.110 \\
5 & 158 & 3 & 64 & — & Adam & 0.002 & 0.266 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Models selected based on Bayesian optimization. Figure 3: Epochs vs. accuracy graphs.