## 4 Experiments and Results
### Experiments and Results
A mini-batch size of 32, maximum training epochs of 20, and initial learning rate of 0.005 were used, with Adam optimization. The segment and hop sizes listed in Table 3 were used to shorten the long ECG signals for the input size. Table 4 presents the experimental results based on the sample lengths of 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. Figure 7 shows examples of signals with various sample lengths. We validated the data for different days using a signal with a single cycle. However, due to the low recognition rate, we expected that increasing the signal length would improve performance. To observe this, we conducted experiments by \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **FRR** & **FAR** \\
**Length** & **Filtering** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline
1500 & - & 98.58 & 98.87 & 98.58 & 98.53 & 0.01 & 1.42 \\ \hline
2000 & - & 99.48 & 99.52 & 99.48 & 99.48 & 0.00 & 0.52 \\ \hline
3000 & SGF, BWPF & 98.94 & 99.13 & 98.94 & 98.88 & 0.01 & 1.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental results when training and validating with data from the same session. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sample** & **ECG** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **FRR** & **FAR** \\
**Length** & **Channel** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline
800 & 1 & 49.27 & 54.19 & 49.27 & 53.14 & 0.95 & 50.73 \\ \hline
800 & 2 & 47.27 & 50.97 & 47.27 & 51.77 & 1.04 & 52.73 \\ \hline
800 & 3 & 42.25 & 45.03 & 42.25 & 51.37 & 1.26 & 57.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results when training and validating using data from different sessions. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Signal Segment Size** & **Signal Hop Size** & **Accuracy (\%)** \\ \hline
800 & 10 & 22.80 \\ \hline
800 & 50 & 20.74 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results of training and validating with data from different sessions without cutting the ECG signals precisely at R-peak positions. gradually increasing the input signal at regular intervals. The reason we can only estimate the results of this nonlinear recognition rate is that the portions of the signal being cut varied, and the signal was ambiguously truncated in the middle, which may have led to a reduction in the contributions of highly recognized parts of the signal throughout the data. Other possible reasons are an increase of unnecessary information and a reduction in the resolution of the input data to cover a broad range of the signal. We aimed to find the optimal length through experimental repetition. Table 5 presents the experimental results of the effects of noise reduction 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. It is observed that B \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **FRR** & **FAR** \\
**Length** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline
1500 & 49.94 & 52.99 & 49.94 & 55.52 & 0.94 & 50.06 \\ \hline
2000 & 55.08 & 54.23 & 55.08 & 59.13 & 0.77 & 44.92 \\ \hline
3000 & 48.95 & 50.07 & 48.95 & 51.65 & 0.97 & 51.05 \\ \hline \hline \end{tabular}
\end{table}