## 2 Related Work
Machine learning and deep-learning algorithms were used to extract features from ECG signal intervals. SVM as well as other machine learning methodologies' deep learning are built on CNN and employ ANN. Because of their different ECG behaviors, MIT-BIH [5], FANTASIA, NSRDB [4] and QT between the datasets can be considered. The suggested convolutional neural network approach in [23] obtained accuracies of 81.33%, 96.95%, 94.73% and 92.85% for the MIT-BIH [5], FANTASIA, NSRDB [4] and QT databases, respectively. In feature selection, Ref. [24] proposed a deep neural network (DNN) entirely based on a denoising auto-encoder (DAE). Recognition rates of 98.10% and 95.67%, respectively, were obtained on self-collected and high-pressure data, and 94.39% on a merged MIT-BIH database and self-collected data. The [23] study's goal was to combine convolutional neural networks with a supervised back-propagation algorithm learning technology to identify an individual based on fiducially extracted features from an ECG Signals LEAD-I signal. The accuracies achieved utilizing 3, 10 and 20 classes on the ECG-ID [4] dataset were of 98.24%, 96.24% and 94%, respectively. The author proposed a new identification method in [25] based on electrocardiograms (EKGs) transformed into a heatmap of a collection of aligned R-peaks (heartbeats), which tends to result in an electrocardiogram. A simple CNN model with high accuracy and a low bit error rate is used as a verification solution to illustrate the feasibility of using EKMs (obtained from ECG traces) for identification. The NSRDB was 99.53% accurate, with a false acceptance rate of 0.02% as well as a false rejection rate of 0.05%. The MIT-BIH and PTB datasets produced very similar results. The system described in [26] employs advanced deep learning methods worn on a single arm to learn from high-level features in ECG raw data. The ECG signal was converted into the two-dimensional domain for CNN to learn the ECG Signals data. Using single-arm ECG, an accuracy of 98.4% was achieved in this study. The concept of transfer learning was used in [27] to fine-tune a PlexNet model using pretrained ResNet and DenseNet models. The proposed ensemble for human identification was tested on publicly available datasets such as PTB and CYBHI, demonstrating its effectiveness with an identification accuracy of 99.66% achieved on both healthy and unhealthy subjects. In [9], two modified VGG-16 approaches and one InceptionResNetV2 design with an additional feature extraction layer and ImageNet weights were proposed. After 5-fold cross-validation with the MIT-BIH normal sinus rhythm database and BIDMC datasets, the model achieved 99.98% accuracy. Ref. [10] suggests an ECG feature framework for human authentication with a cancelable ECG method. A transfer learning method was used to extract ECG features, followed by a recommended cancelable technique to defend deep network templates. Finally, a support vector machine (SVM) achieved 99.26% accuracy on the ECG-ID dataset and 99.39% on the PTB dataset for authentication. In [28], several machine learning methods such as the SVM of different kernel parameters and deep neural network designs were used to identify the ECG-ID dataset of QTc augmentation (e.g., RNN and CNN). As a result, the classifier performance was compared with and without QTc augmentation. The study [29] used the ASCERTAIN database, which included five personality scale items and emotional self-assessments from 58 users, along with data from electro-encephalographies, electrocardiograms, galvanic skin responses and facial activity. The scheme for transforming electrocardiogram signals into various wavelets and then applying them to the ensemble deep learning model outperforms CNN or ResNet18 techniques. The study had a 98.10% success rate. This research proposed an ECG identity verification technology based on the method in [6], which relies on the recurrence graph and transfer learning. The recurrence plot was used to create two-dimensional images from one-dimensional time series.