## 4 Experiments and Results
### Experiments and Results
Here, we investigated the experimental results of other methods. Table 11 shows the experimental results of other methods when training and validating with data from different sessions. The CZE-SVM utilized cepstral coefficients, zero crossing rate, and entropy for feature engineering, and several support vector machines were used for classification [50]. The EECGNet was designed based on PCANet. PCANet is a cascaded linear network that uses principal component analysis (PCA) filters for convolutions. The outputs are binarized, combined with weights, and then used to calculate histograms. The final results are obtained through the following process [51]: EECGNet-1 is configured with three stages, a patch size of three, four filters, and a block overlap rate of 0.5. EECGNet-2 is configured with four stages, a patch size of five, four filters, and a block overlap rate of 0.5. EECGNet-3 is configured with four stages, a patch size of seven, four filters, and a block overlap rate of 0.5. The EDNet showed more higher accuracies than other methods. We tested the CZE-SVM using code obtained from the author's open repository [52]. It exhibited very poor accuracy, ranging from 13.19% to 17.75%. The method performed inadequately in normalizing ECG signals that vary from day to day. Other methods also demonstrated poor accuracy with day-to-day variations in ECG signals, even after applying a band-pass filter. EECGNet has shown accuracy rates ranging from 39.37% to 55.40%. There are significant accuracy gaps depending on its parameter settings. There are many parameters; therefore, we were able to explore only a limited set of parameters. Compared to the accuracies shown in Table 9, our EDNet-P and EDNet-M methods achieve at least 3.39% higher accuracy than the other methods. \begin{table}
\begin{tabular}{c c c} \hline
**Combination of Models** & **Fusion Method** & **Accuracy (\%)** \\ \hline ResNet, ResNet(aug) & Multiplication & 57.68 \\ \hline ResNet, ResNet(aug) & Additon & 58.25 \\ \hline ResNet(aug), Inception-ResNet-v2 & Multiplication & 56.32 \\ \hline ResNet(aug), Inception-ResNet-v2 & Addition & 55.94 \\ \hline ResNet, Inception-ResNet-v2 & Multiplication & 58.38 \\ \hline ResNet, Inception-ResNet-v2 & Addition & 57.52 \\ \hline \end{tabular}
\end{table} 
Table 10: Experimental results of various network combinations when training and validating with data from different sessions. Table 12 shows the experimental results of EECGNet [51] under various parameters. Some training trials with parameters are failed due to the memory lack issue. There are possibilities that other methods can be more robust by adjusting the parameters. There are many parameters; therefore, we were able to explore only a limited set of parameters. To enhance model interpretability, methods such as a class activation map have been proposed. In this paper, the ensemble approach, which uses multiple independent models, offers the possibility of interpretation across various features without relying solely on a single model's interpretation. Additionally, by incorporating separate subnetworks for interpretation within the ensemble, it is anticipated that the decision-making process can be further analyzed in the future. When performing personal identification using data collected on the same day, a fairly high recognition rate is achieved. In this paper, only data from one day was used for training, but it is expected that increasing the number of days in the training data will further improve the recognition rate. Although the recognition rate was \(59.11\%\) when using data from one day for training and data from other days for validation, it is anticipated that further improvements will allow for accurate personal identification while reducing the variability of training data acquisition.