## 1 Introduction
Second, the method of dividing and using the blind with constant duration of each segment, which is the period fixed to the ECG signal, can lower the complex calculation and improve the performance. This approach should incorporate the cyclic characteristic of the ECG mentioned above. Third, a noise removal step is not applied because the noise is not applied with a circulatory stop. Fourth, a 2D-CNN is used by transforming it into a power spectral density, which is the frequency domain of signals. Finally, eight open databases are combined into a database. [PERSON] [16] removed noises through a band-pass filter in a preprocessing step for ECG signals and divided the cycle by a constant time with respect to the R-peak. After transforming the data into images using four types of spatial representation (namely, CWT, Gramian angular field (GAF), phase-space trajectories, and recurrence plots), a CNN consisting of three convolutional layers, activation function ReLU, a max pooling layer, a fully-connected layer, and a softmax layer was applied for a comparative analysis of the accuracy and equal error rate (EER) of ECG-based biometric recognition. [PERSON] [17] used a CNN model of transfer learning for various time-frequency representations to examine the performance of ECG biometric recognition. Four transfer learning models using MFCC, spectrogram, log spectrogram, mel spectrogram, and scalogram were employed as a time-frequency representation method. [PERSON] [18] proposed a personal identification method where multidimensional features are extracted by adjusting the bi-cubic 2D size for maintaining the data values and converting the ECG signals into a spectrogram. Noises are removed through preprocessing, and the signals are divided into a cycle consisting of a P wave, QRS complex wave, and T wave for personal identification. The divided signals are converted into a spectrogram to reduce the image size by 1/2 and 1/4 for identifying users. [PERSON] [19] proposed a method of classification by adding the output of LSTM cells for personal identification using ECG signals. In the proposed model, the variations in bit could be observed because the signals were divided into smaller units considering the fluctuations in bit. Moreover, personal identification was performed according to various window lengths. [PERSON] [20] proposed a personal identification method based on a 2D coupling image using the cycle information of ECG signals. A 2D coupling image uses a CNN consisting of 12 convolutional layers and six max pooling layers for ECG-based user recognition. [PERSON] [21] proposed an end-to-end deep neural network (DNN) for ECG-based authentication. The first model was designed as a 1D-CNN consisting of four convolutional layers, two max pooling layers, two fully-connected layers, and one max pooling layer. The convolution product of a CNN can efficiently extract morphological features from time-series or image data. The second model was designed as ResNet-Attention. It combines the output of the first class consisting of two convolutional layers, a normalization layer, a ReLU layer, and a dropout layer, with that of the second class consisting of two normalization layers, two ReLU layers, two dropout layers, and two convolutional layers to be used as the input of Attention. Attention inspects the user authentication performance through two dense layers, a ReLU layer and a softmax layer. In this study, personal identification is carried out based on the ensemble of LSTM and CNN by using ECG. The CU-ECG database constructed at Chosun University is used in the study. Non-handcrafted fiducial-based methods involve the detection of the R-peak of signals and division at certain intervals. Furthermore, short-time Fourier transform (STFT), scalogram, Fourier synchrosqueezed transform (FSST), and wavelet synchrosqueezed transform (WSST) are used as time-frequency representations to convert these into images.