## 1 Introduction
Moreover, the sensors need to be accurately attached to the muscle position. ECG shows the electrical signals of the heart's rhythm through a micro-current, which consists of the P wave (atrial depolarization), QRS complex wave (ventricular depolarization), and T wave (ventricular repolarization). The ECG signal is unique to each individual and depends on the size and position of the heart, gender, and age. The 12-lead ECG involves the measurement of signals from both the wrists. It is utilized for medical diagnosis as well as personal identification. Because ECG measures a signal generated inside the human body, it is difficult to replicate or tamper and is unaffected by environmental transformations. Furthermore, all living humans have these signals [6]. Identification based on physiological characteristics such as EEG, EMG, and ECG has been researched extensively for security methods based on personal signals because of its various advantages. However, this study conducted personal identification using ECG, which displays all the seven characteristics of biometric recognition. In recent years, research on personal identification using deep learning has been conducted actively. The first study on user recognition using ECG was conducted by [PERSON] [7]. It can be classified into handcrafted and non-handcrafted fiducial-based methods. Handcrafted feature extraction methods are based on the characteristics of ECG, namely, the ECG signal amplitude, time interval (ST interval, PQ interval, and QS interval), peak (minimum and maximum peaks of P, Q, R, S, and T), and angle [8; 9]. This method is a type of machine learning where features are extracted directly based on the learning performed. [PERSON] [10] extracted 15 time-interval features by determining the maximum local value based on P, R, and S peaks, tracking the slope, and identifying the position of the minimum curve radius for personal identification using ECG. [PERSON] [11] used the continuous wavelet transform (CWT) for extracting features as a representation of the time domain used for signal processing tasks such as image compression and pattern recognition. Personal identification was performed on the MIT/BIH arrhythmia database using ID matching by applying the encryption technology to ECG signals through CWT. [PERSON] [12] used ensemble empirical mode decomposition (EEMD) and Welch spectrum analysis to extract intrinsic mode function (IMF) spectral features for obtaining the morphological and spectral information of signals. However, handcrafted feature extraction methods entail problems because the detection of peaks causes high variability in signals [13]. Furthermore, these methods display reduced performance while removing noises or extracting features. Consequently, the emergence of deep learning resulted in the increased use of long short-term memory (LSTM). LSTM improves the performance of time-series and convolutional neural networks (CNNs), which display remarkable image classification performance. Deep learning extracts features through learning. Therefore, features need not be extracted directly as in handcrafted feature extraction methods. Non-handcrafted fiducial-based methods need to detect the R-peak for the signal division because characteristic points are not used based on the overall form of ECG signals. For personal identification based on ECG, [PERSON] [14] proposed CNN-based deep-ECG using the PTB database. They performed preprocessing, CNN feature extraction, and identification in that order. A notch filter, infinite impulse response (IIR) filter, and third-order high-pass filter are used in the preprocessing step. The CNN consisting of six convolutional layers, a dropout layer, a fully-connected layer, and a softmax layer performed personal identification. [PERSON] [15] proposed five approaches for personal identification using ECG. First, signals are distinguished using the cyclic characteristics of ECG signals.