## 1 Introduction  
As early as 1977, electrocardiogram (ECG) was identified for its potential for biometric authentication [1]. ECG is relatively easy to acquire, for example, using finger sensors [2]. Many methods have been implemented of ECG identification using feature extraction based on time, amplitude, and frequency [3; 4; 5; 6], as well as using machine learning [7; 8; 9]. Nevertheless, ECG biometrics still cannot be fully implemented in real-time security systems due to their high computational time and low accuracy. ECG identification using multivariate analysis feature extraction has been reported in [3]. Using principal components analysis (PCA) and soft independent modeling of class analogy (SIMCA), the ECG identification results vary from 90% to 100%. The accuracy of this method depends on two main factors, including the number of features and the type of lead used to obtain an ECG signal. In [3], they used 12 leads and 30 types of time-based ECG features. Around 50 samples were evaluated from 20 ECG records. Although ECG identification can be performed with just one lead, this research needs to use at least three leads to improve identification accuracy [3]. In [5], the number of leads and the number of ECG features were reduced; specifically, the number of leads has been reduced one, and the number of features has been reduced to seven. After preprocessing and feature extraction, two different methods were normally used for identification purposes, i.e., template matching (TM) and the decision-based neural network (DBNN). Evaluation of 400 ECG beats from 20 people revealed that the accuracy of TM and DBNN was 95% and 80%, respectively. Moreover, combining both methods produced 100% identification accuracy. In 2005, the ECG-ID database became publicly accessible [10]. The database contains ECG recording files of 90 people using a single ECG lead. Recorded ECG signals from the same person are stored in one folder. Each folder contains two or more ECG recording files, except the 74 th folder, containing only one record. After preprocessing and time-based feature extraction, ECG identification is conducted using PCA, linear discriminant analysis (LDA), and majority vote classifier (MVC). Moreover, the accuracy of this method is around 96%. Apart from using the database of normal ECG heartbeats, ECG biometrics have also been tested using arrhythmia ECG signals [11]. Using test data from the ECG records of 47 patients in the MIT-BIH database, 91.1% accuracy was reported. They applied five stages of blind signal processing (BSP), including preprocessing, wavelet transform, autocorrelation, component selection, and parallel one-dimension convolutional neural networks (1-D CNN). A similar identification method can be applied to other databases, including ECG records from the ECG-ID and MIT-BIH databases [8; 9]. Bidirectional gated recurrent units (BGRUs) have been used to identify 47 patients from the MIT-BIH database and 90 ECG healthy people from the ECG-ID database with an accuracy of 98.40% and 98.60%, respectively [8]. When, instead, a PCA neural network (PCANet) was used, accuracies of 100% and 97.75% were achieved when tested on ECG-ID and MIT-BIH databases, respectively [9]. Recently, depthwise separable convolution (DSC) has been applied to a CNN model to reduce computation by reducing the number of model parameters and arithmetic calculations. DSC has been utilized to improve CNN model quality in image classification [12] and neural machine translation [13]. For cardiac arrhythmia classification, DSC has been tested on 16 ECG classes of the MIT-BIH database, outperforming other methods [14]. Besides DSC, the CNN model can be further optimized using the residual technique. The residual technique has been used in CNN to simplify the training process and increase accuracy, as is evident in image classification [15] and ECG classification [16]. Residual DSC has been implemented in Xception [12] and PydMobileNet [17].