## 3 Proposed Personal Identification Based on Ensemble of LSTM and 2D-CNN
### Proposed Ensemble-Based Personal Identification  
The performance of various models can be compared by training these and selecting those with a higher performance. However, when an individual model with remarkable performance is used, the performance improvement in other models can be omitted. Thus, the performance can be improved further by combining different models. An ensemble is a technique for improving performance by combining different models. It can demonstrate performance higher than that of the individual models. Computation, representation, and statistics can be improved by deep learning models and ensembles [32]. An ensemble uses the voting method, average, maximum, and multiplication for the output values of each model to predict the final results. In this study, the deep learning models LSTM and 2D-CNN are combined for personal identification. The numbers of hidden layers and units are increased to enhance the classification accuracy, and LSTM layers are added to deepen the LSTM. Furthermore, 1D ECG signals are transformed to 2D data by using time-frequency representation methods. Three pre-trained CNN models (namely, GoogleNet, VGG-19, and ResNet-101) are used. An ensemble method for combining the output values of two models is used to enhance the performance of an individual model. An ensemble performs personal identification by determining the final prediction results using the output values of each model for an identical input. Personal identification using ECG can be divided into three steps: signal preprocessing, feature extraction and learning, and personal identification (see Figure 15). Figure 14: Residual Connection. Figure 12: Structure of VGG-19. Figure 13: Structure of ResNet-101. In the first step of signal preprocessing, noises need to be removed for maintaining the shape of ECG signals. This is because the signals can be distorted by various noises during measurement. In an ECG, breathing, friction between skin and electrode, noise from muscles, and noise from contact between electrode and power line are included during measurements. In this paper, the noises of ECG signals were removed using a low-pass filter. A low pass filter is a filter that passes through a frequency signal below the cutoff frequency. This filter is used to remove high frequency components such as muscle noise, 60 Hz power line noise, and electrode contact noise and to soften the signal. These noises are removed through a low-pass filter as shown in Figure 16a,b. In Figure 16b, where noises have been removed, the baseline of the signal shifts above or below the \(x\)-axis of the signal. Fluctuations in the baseline are low-frequency vibrations generated by the breathing, sweat, or movement of a subject. These cause variations in the impedance between electrode and skin. The baseline functions as the criteria for detecting characteristic points of ECG signals. Noises must be removed from the baseline because the morphological characteristics of ECG signals cannot be identified if the fluctuations in the baseline occur. Figure 16c shows the signals wherein the baseline has been calibrated with respect to zero, and Figure 16d shows the standardized signals. ECG signals consist of a P wave, QRS complex wave, and T wave and include various cycles (see Figure 17a). Signals are divided to extract features of the ECG signals. As shown in Figure 17b, the R-peak is detected to divide the signal into a cycle with respect to the detected R-peak. The second feature extraction and learning stage is an important step that affects classification performance results. For this purpose, electrocardiogram data is applied to LSTM and 2D-CNN to extract and learn features, respectively. In order to use 2D-CNN, one-dimensional ECG signals need to be converted into two-dimensional images. Therefore, it is converted through four time-frequency representations such as STFT, Scalogram, FSST, and WSST, which are transformation methods, and a pre-trained model, GoogleNet, VGG-19, ResNet-101 is used.