# Semi-Supervised Domain Adaptation for Individual Identification from Electrocardiogram Signals
## 5 Conclusions  
We propose a semi-supervised domain adaptation approach for individual identification using ECG signals. When acquiring ECG signals, the placement of electrode patches plays a critical role. Consistent electrode placement is essential to ensure the reliability of ECG signals for individual identification. However, maintaining a consistent placement of ECG electrode patches during data acquisition is challenging. Therefore, it is necessary to collect ECG data from various electrode placements. However, this process is labor-intensive and time-consuming because it often requires multiple attempts. To streamline this process and reduce the required labor and time, a semi-supervised domain adaptation approach for individual identification using ECG signals can be effectively used. This method works with a comprehensive dataset of original ECG signals and a smaller set of ECG signals from various placements to accommodate signal variations in the CycleGAN. In this approach, the ECG signals were transformed into time-frequency representations to train the CycleGAN. The trained generator was then used to produce ECG signals via expanding the dataset to include signals from different placements. To make the signals \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Gender**} & \multicolumn{2}{c}{**Epochs of**} & \multirow{2}{*}{**Accuracy**} \\  & & & **CycleGAN** & \\ \hline \multirow{2}{*}{Without generated data} & Men & & 99.33 \\ \cline{2-4}  & Women & & 97.83 \\ \hline \multirow{2}{*}{With generated data} & Men & & 99.17 \\ \cline{2-4}  & Women & & 98.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of test accuracy between men and women. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Epochs of**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**F1-Score**} \\  & & & & & \\ \cline{3-5}  & 100 & 94.11 & 95.63 & 94.11 & 93.78 \\ \cline{2-5}  & 200 & 92.62 & 94.35 & 92.62 & 92.18 \\ \cline{2-5} CycleGAN-based & 300 & 90.06 & 93.06 & 90.06 & 89.30 \\ \cline{2-5} MobileNet-V2 [10] & 400 & 91.70 & 94.24 & 91.70 & 91.56 \\ \cline{2-5}  & 500 & 93.25 & 94.96 & 93.25 & 92.69 \\ \cline{2-5}  & 600 & 93.43 & 95.14 & 93.43 & 93.03 \\ \hline Ours & 600 & 97.06 & 97.32 & 97.06 & 96.82 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of test accuracy with other methods. of the original and generated data similar, a weighted average was applied. Both original and generated signals were used to train the classifier for individual identification. This approach can also be applied to the classification of ECG signals from different sensors. For this experiment, we used the PTB-ECG dataset. Our findings indicate that the proposed method yields a higher accuracy than using only the original ECG signals for training the classifier and other methods. In a future study, we will investigate methods to enhance the domain transfer between the generated and original data with the aim of improving the classification accuracy. This involves analyzing the generated data and modifying the structure of the generator. Conceptualization, Y.-H.B. and K.-C.K. ; Methodology, Y.-H.B. and K.-C.K. ; Software, Y.-H.B. and K.-C.K. ; Validation, Y.-H.B. and K.-C.K. ; Formal Analysis, Y.-H.B. and K.-C.K. ; Investigation, Y.-H.B. and K.-C.K. ; Resources, K.-C.K. ; Data Curation, K.-C.K. ; Writing-Original Draft Preparation, Y.-H.B. ; Writing-Review and Editing, K.-C.K. ; Visualization, Y.-H.B. and K.-C.K. ; Supervision, K.-C.K. ; Project Administration, K.-C.K. ; Funding Acquisition, K.-C.K. All authors have read and agreed to the published version of the manuscript. This research was supported by the Basic Science Research Program via the National Research Foundation of Korea (NRF), funded by the Ministry of Education (No. 2017R1A6A1A03015496). Not applicable.