## 5 Conclusions  
This study performed personal identification based on the ensemble of LSTM and 2D-CNN with ECG signals. ECG-based personal identification is based on a comparison of the ECG of a user with that of registered users. ECG uses unique signals of each person, which vary depending on the position and size of the heart, gender, and age. Thus, individuals can be identified with over 90% accuracy based on the characteristics of ECG signals. In addition to personal identification, ECG is being widely used in the medical field for predicting and diagnosing heart-related diseases. Therefore, personal identification using ECG signals as well as ECG-based health monitoring technology that enables remote examination of heart diseases such as cardiac arrest or arrhythmia are likely to be developed. ECG signals are accompanied by different types of noises because these are physiological signals measured through microcurrents. Therefore, distorted signals are removed through filters to enable accurate assessment or diagnosis. Because the adjusted baseline is the reference for detecting the characteristic points of ECG signals, it becomes difficult to identify the morphological characteristics of ECG signals if the baseline is not calibrated to zero. Accordingly, noise removal and baseline fluctuation adjustment were performed as a preprocessing step. In order to classify the one-dimensional ECG signal with noise removed, two LSTM layers with higher accuracies were used as a result of comparing and analyzing one LSTM layer and two LSTM layers. As such, the more layered the classifica \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**CU-ECG**} & \multicolumn{1}{c}{**Time–Frequency Representation**} & \multicolumn{1}{c}{\
\begin{tabular}{c} **2D-CNN** \\ **Validation Accuracy** \\ \end{tabular} } & \multicolumn{1}{c}{
\begin{tabular}{c} **LSTM-2D-CNN Ensemble** \\ **Validation Accuracy** \\ \end{tabular} } \\ \hline \multirow{4}{*}{**GoogleNet**} & STFT & 95.09\% & 98.37\% \\ \cline{2-4}  & Scalogram & 95.69\% & 97.87\% \\ \cline{2-4}  & FSST & 96.25\% & 98.58\% \\ \cline{2-4}  & WSST & 95.48\% & 98.73\% \\ \hline \multirow{4}{*}{**VGG-19**} & STFT & 94.86\% & 97.90\% \\ \cline{2-4}  & Scalogram & 94.18\% & 97.93\% \\ \cline{2-4}  & FSST & 94.52\% & 97.93\% \\ \cline{2-4}  & WSST & 95.12\% & 98.52\% \\ \hline \multirow{4}{*}{**ResNet-101**} & STFT & 97.67\% & 98.73\% \\ \cline{2-4}  & Scalogram & 97.25\% & 98.73\% \\ \cline{2-4}  & FSST & 96.04\% & 98.43\% \\ \cline{1-1} \cline{2-4}  & WSST & 95.24\% & 98.49\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Accuracy of LSTM and 2D-CNN-based ensemble. tion performance accuracy can be improved, but there is a problem that the structure can be complicated. In this paper, we propose an ensemble, so we use two layers without building more layers of LSTM to avoid complex structures. In addition, the 1D ECG signal is represented by the image using the Short-Time Fourier Transform (STFT), Scalogram, Fourier Synchrosqueezed Transform (FSST), and Wavelet Synchrosqueezed Transform (WSST) as a time-frequency representation. For performance results, the performance of each model of the composite multi-neural network with excellent image classification performance was confirmed using GoogleNet, VGG-19, and ResNet-101, and the performance of each model of the LSTM and 2D-CNN was improved through the ensemble method. To conduct the experiment, the CU-ECG database constructed by Chosun University used data containing 100 subjects, 89 men, and 11 women, who were in comfortable postures. The results of two LSTM neural networks showed that the highest performance was 95.12% when epoch was set at 100 and the minibatch size at 128, and the performance of 2D-CNN was the highest at 97.67% for ResNet-101. Finally, the performance of each model of LSTM and 2D-CNN was improved by an ensemble method, with a personal identification performance of at least 1.06% to a maximum of 3.75% compared to that of a single model. Conceptualization, J.-A.L. and K.-C.K.