###### Abstract
The discriminator must learn to classify images as real or refined. They show that the improved realism enables training of better models for gaze and hand pose estimation by adding refined tagged images to the training set. The ECG heart rate classification task [6] similarly demonstrated that classification performance could be improved by adding synthetic ECG heartbeats generated from a standard GAN to a training set. It also generates synthetic ECG signals that can be used in training; however, unlike the previous work, we use a simulator to improve the deep generative model's ability to generate synthetic labeled ECG heartbeats that have tabular data. Another recent study proposed DoseGAN [7], which is an attention-gated GAN model. This is proposed to increase the complexity of the model, improve model learning, and increase the complexity of the model. It also aims to reduce network redundancy by focusing on relevant anatomy. Classical methods of classification at the level of ECG beats focused on extracting interval characteristics and using previous knowledge of the morphology of the ECG [8]. Each ECG signal is separated into heartbeats using heartbeat detection techniques [9]. For each heartbeat, the characteristics related to heartbeat intervals and ECG morphology are calculated, then the combined characteristics are introduced in supervised machine learning models based on linear discriminants (LD) [10]. The application of deep learning models to ECG classification has become popular and has been applied to many tasks, such as arrhythmia detection at the cardiologist level [11] and classification of ECG heartbeats [12; 13; 14; 15]. The state-of-the-art method for classifying EKG heartbeat levels [14] has recently shown that superior results are achieved when applying the ResNet convolution model that classifies each heartbeat class separately in this work to the training of such deep learning systems that have been trained with additional synthetic EKG heartbeats generated from the previously proposed generative models. medGAN (medical generative adversarial networks) is a well-known tool for constructing practical synthetic medical records. They combine autoencoders and GAN architecture to produce high-dimensional discrete values [16]. The authors used autoencoders in medGAN to delegate samples to learn outstanding features. GAN was equipped with previously qualified autoencoders to produce distributed patient data instead of directly generating patient records. Synthetic healthcare data has also been collected using generative adversarial networks [17]. To capture the connection between consecutive diagnostic data, the authors used one-dimensional (1-D) convolutional neural networks. Convolutional autoencoders were then used to connect discrete and continuous values. PATE (Private Aggregation of Teacher Ensembles) is another GAN model for generating synthetic data that preserves the generator's differential privacy, which is critical for biomedical data [18]. The generator in PATE-GAN is similar to that of a regular GAN model, but the discriminator is fitted with a PATE mechanism, resulting in K-teacher discriminators and asymmetrical training. [PERSON] et al. [19] used GAN to synthesize natural heart sounds. Neuron-based generative models for SSVEP classification in EEG data is one of the models [20]. For this, the researchers used deep convolutional generative adversarial networks and variational autoencoders. Using the bidirectional recurrent neural network, recent work has also yielded efficient results for producing single synthetic biomedicals [21]. Synthetic data are artificial data generated by replicating the statistical characteristics of the original data. There are many methods of generating synthetic data. When real data is not available, synthetic data can be created according to distribution known by the data analyst or the best-fit distribution can be determined for the original data.