## 4 Experiments and Results
### Experiments and Results

Table 4: Experimental results on the effect of sample length of ECG signals when training and validating using data from different sessions. Figure 7: The examples of signals with various sample lengths. while the SGF is observed to significantly reduce accuracy. Figure 8 shows the results of the SGF and BWPF of an ECG signal. If the SGF's polynomial order is higher, it is closer to the original signal, and if the SGF's frame length is shorter, it is also closer to the original signal. We set the parameters to minimize distortion of the signals while providing a slight smoothing effect intuitively. The most distorted part of SGF is observed in QRS complexes. The result of filtering can be improved by adjusting the other SGF's parameters, but the result will be very similar to the original signal. Figure 8: The results of applying the SGF and BWPF on an ECG signal. Table 6 presents the experimental results of the effects of the sample length when applying only BWPF during training and validation using data from different sessions. The ECG signals were transformed into time-frequency representations using a continuous wavelet transform, and the pretrained ResNet-101 was trained. The mini-batch size was 30, with a maximum of 20 epochs, and an initial learning rate of 0.0001, with no data augmentation being used. Adam optimization was also applied. Table 7 presents the experimental results for the effect of expanding the input channels for the ECG signals during training and validation using data from different sessions. The ECG signals were transformed into time-frequency representations using a continuous wavelet transform, and the pretrained ResNet-101 was trained. The mini-batch size was 30, with a maximum of 20 epochs, and an initial learning rate of 0.0001, with no data augmentation being used. Adam optimization was also applied. Table 8 presents the experimental results for various single pretrained models when training and validating using data from different sessions. The ECG signals were transformed into time-frequency representations using a continuous wavelet transform. The models trained included pretrained ResNet-101, pretrained ResNet-101 with augmentation, and pretrained Inception-ResNet-v2. The mini-batch size was 30, with a maximum of 20 epochs, an initial learning rate of 0.0001, and Adam optimization. Figures 9-11 depict the training processes of each pretrained deep neural network when training and validating with data from different sessions. As shown in the figures, the validation utilized 20% of the test set. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample Length** & **Filtering** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** & **FRR (\%)** & **FAR (\%)** \\ \hline
1500 & BWPF & 45.59 & 52.76 & 45.59 & 50.32 & 1.11 & 54.41 \\ \hline
2000 & BWPF & 55.65 & 56.41 & 55.65 & 58.30 & 0.75 & 44.35 \\ \hline
3000 & BWPF & 54.95 & 55.19 & 54.95 & 58.43 & 0.77 & 45.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experimental results on the effects of sample length when applying only BWPF during training and validation using data from different sessions. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sample Length** & **ECG Channel** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** & **FRR (\%)** & **FAR (\%)** \\ \hline
2000 & 1 + 2 & 46.95 & 49.13 & 46.95 & 52.07 & 1.05 & 53.05 \\ \hline
2000 & 1 + 2 + 3 & 48.16 & 53.35 & 48.16 & 53.81 & 1.00 & 51.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Experimental results on the effect of expanding input channels for ECG signals during training and validation using data from different sessions.