## 4 Experiment and Results Analysis
### Experimental Method and Results  
In this section, the performance of personal identification using LSTM and 2D-CNN from ECG signals is analyzed. LSTM was used for analyzing 1D ECG signals, and the performances of LSTM layer and two LSTM layers were analyzed comparatively. The initial learning rate of the experiment was 0.01. Adam was used as an optimization function for minimizing the error between the predicted and actual values. Epochs of 30, 50, 60, and 100 and a mini-batch size of 128 were applied repeatedly to examine the performance (see Figure 19). In addition, 100 hidden layers were used for LSTM. The LSTM-based personal identification accuracy for the CU-ECG database was highest (95.12%) when the epoch and mini-batch size were set to 100 and 128, respectively.  
Because physiological signals such as ECG signals are affected by various types of noises, the signals were transformed into 2D images to be applied for personal identification using CNNs. Figure 20 shows the images transformed by STFT, scalogram, FSST, and WSST for a subject in the CU-ECG database.  
Figure 18: Flowchart of proposed ensemble-based personal identification.  
The personal identification accuracy for the transformed image was examined using a 2D-CNN. The transfer learning models GoogleNet, VGG-19, and ResNet-101 were used for the 2D-CNN. The data were converted to 224 \(\times\) 224 to be used as input. The following were the settings for GoogleNet to conduct an experiment using the CU-ECG database: an initial learning rate of \(1\times 10^{-4}\), Adam as an optimization function, an epoch of 30, and mini-batch size of 64. Meanwhile, the following were the settings for VGG-19: an initial learning rate of \(1\times 10^{-4}\), Adam as an optimization function, an epoch of 20, and a mini-batch size of 32. ResNet-101 was applied in a similar manner as VGG-19. Furthermore, the accuracy of personal identification using ECG signals based on the proposed ensemble was examined rather than separately examining the performance of LSTM and of CNN. Table 1 presents the accuracy of personal identification based on the ensemble of LSTM and 2D-CNN and that based on 2D-CNN for the CU-ECG database. GoogleNet demonstrated the highest performance of 96.25% in FSST, whereas VGG-19 demonstrated the highest performance of 95.12%. ResNet-101 demonstrated the highest performance of 97.67% in STFT. The accuracy of the proposed model was 12.88% and 12.88%, respectively.  
Figure 19: Accuracy of LSTM-based personal identification.  
Figure 20: Time–frequency representation of a subject with regard the CU-ECG database signal.  
of personal identification using an ensemble of LSTM and 2D-CNN was examined using GoogleNet, VGG-19, and ResNet-101 for STFT, scalogram, FSST, and WSST expressed with a time-frequency representation. The ECG signal was converted into the two-dimensional time-frequency domain through LSTM and four transformation methods, and it can be seen that the personal identification performance results using GoogleNet, VGG-19, and ResNet-101 are all excellent. However, it can be confirmed that the individual identification performance through the ensemble method using the maximum value by multiplying the score values of each model proposed in this paper is superior to the performance of the single model. GoogleNet demonstrated the highest performance in FSST, where the result of an ensemble showed an improvement of 2.33%. Furthermore, VGG-19 demonstrated the highest performance in WSST with an improvement of 3.4% from the individual models. Meanwhile, ResNet-101 demonstrated the highest performance in STFT, with an improvement of 1.06% from the individual models.