## 2 Related Work
The GoogLeNet model was modified for ECG identification, achieving 96.26% accuracy on the PhysioNet D1 database. DenseNet, Xception and ResNet are well-known convolutional neural network models that were used in this study by [30]. For evaluation, the PTB-ECG dataset was used, and the accuracy obtained by the opened CNN model with a spectrogram was 98.85%, ResNet101 was 97.95%, and DenseNet201 was 98.99% with MFCC. Numerous available public ECG and biometric databases were used to test the system. The goal of the research in [31] was to develop a secure multi-modal biometric technology using a different fusion level of convolution neural network (CNN) as well as the Q-Gaussian multi-support vector machine (QG-MSVM). That method was 99.83% accurate. In this study, the proposed technique in [32] was tested on the PTB-ECG and CU-ECG datasets. PTB identification rates for ResNet, DenseNet, VGGNet and Xception were 97.02%, 97.33%, 97.83% and 97.16%, respectively, and the CU-ECG identity rates were 90.56%, 93.49%, 92.11% and 92.8%, respectively. The model [33] was evaluated against two publicly available datasets, the ECG-ID data (ECGID) [4] and the MIT-BIH arrhythmia database [5]. According to the experimental results in [33], the proposed method using the BGRU design, which is a bifacial mixture of the recurrent neural network and GRU cell units, achieved a high classification accuracy of 98.55%. The system was built using deep learning algorithms such as convolutional neural networks (CNNs) and a long short-term memory (LSTM) architecture with a customizable activation function. The authors in [34] evaluated the proposed method with off- and on-person datasets such as ECG-ID, PTB, CYBHi and UofTDB, and obtained accuracies of 99.49%, 99.62%, 94.86% and 95.36%, respectively. A methodology that relies on LSTM was created in this research [35] for human recognition using an ECG signal. The MIT-BIH arrhythmia [5], CYBHi, ECG-ID [4] and PTB databases have all been thoroughly tested to ensure the model's efficacy. The accuracy for 290 persons in the PTB database is 97.3%. Other databases produce similar results, with CYBHi obtaining an accuracy of 79.37%. Using a BLSTM network, the suggested data-independent approach in [36] achieves a relatively better identification accuracy for each database. The proposed technique in [37] was tested using deep learning methods such as CNN and LSTM on self-collected databases S1 and S2, with the experimental results showing an EER% of 0-10.13% for the S2 database and the mixed S2 and S1 datasets, 0-5.31% for one-day enrollment, and 0.033-3.93% and 0-1.35% for two-day enrollment. The study in [38] proposed using an electrocardiogram and an ensemble of CNN and LSTM for personal identification, using the CU-ECG database created by Chosun University, which showed an accuracy greater than 90% in identifying individuals based on ECG signal characteristics. The proposed method in [39] used a 1D CNN model to eliminate incorrect heartbeats from low-quality ECG recordings, while an attention-based bidirectional LSTM was employed to learn high-level identification features. Experimental results on the publicly available ECG-ID [4], AHA [5], LT-AFB, AFDB, STAFF-III and MIT-BIH datasets showed accuracies of 98.84%, 99.2%, 97.07%, 99.5%, 97.94% and 97.39%, respectively, with 97.54% and 99.70% accuracy obtained on ECGID and the MIT-BIH arrhythmia datasets, respectively. In [40], the data are transformed from the temporal domain into the wavelet domain, and then a parallel 1D-CNN is employed to automatically discover multiscale feature hierarchical structures from the raw wavelet data. The model performed well on eight ECG datasets, with an accuracy rate of 93.5%. In [41], an EMG-based individual identification strategy based on continuous wavelet transform and Siamese systems was proposed. The MYO armband from Thalmic Labs was used to capture EMG signals. Experiments show that, using this method, the identification results for 21 subjects can reach 99.285%.