###### Abstract
The rate or rhythm of the heartbeat provides the defining feature of the heart disorder known as arrhythmia. A variety of heartbeat patterns exist, including those that are irregular, too fast, or too slow. Unlike bradycardia, which is associated with extremely slow heartbeats, tachycardia is a condition where the heartbeat is too fast [3]. To deal with patients with arrhythmia and other databases with irregular heartbeats, such as ECD-ID [4] and MIT-BIH [5], deep learning infrastructure techniques with adaptive feature extraction and intrinsic classification systems have recently gained popularity. However, these techniques usually require a large amount of training data. The paper [6] describes a technique for transfer learning that employs convolutional neural networks. This technique involves integrating information from the GoogLeNet [7] model, which was used for identification, pretraining the model with the \"fine-tune\" concept, and adding three adaptive layers behind the initial feature layer. An ensemble method of pretrained models was then trained for ECG signal classification using the ECG signal. The backbone consisted of two different VGG-16 [8] models and an Inception-ResNetV2 model with feature extracting layers and ImageNet weights [9]. The preprocessing of input ECG signals was accomplished using image processing techniques. The deep transfer learning method was used for feature extraction, and SVM was used for the identification of subjects [10]. The difference between deep learning and conventional methods is that deep learning techniques automatically learn intrinsic patterns from big data, whereas conventional methods heavily rely on designers' prior knowledge and make it practically impossible to take advantage of large amounts of data [11]. However, in the case of conventional feature extraction techniques such as the discrete wavelet transformation (DWT), they retrieve valuable information in the time-frequency domain and represent a promising method for extracting the features from heartbeat responses. DWT generates a significant number of coefficients to describe the signal details and approximations at different scales. Therefore, selecting coefficients requires minimizing the feature size [12]. Consequently, deep network techniques extract useful data from raw information, and the extracted data are both relatively small and better than the original information, whereas conventional features such as DWT analyze data in the time and frequency domain to extract intrinsic features by decomposing the data on multiple scales. The major contributions of this research are presented below  
1. Integration of discrete wavelet transforms with one-dimensional convolutional recurrent neural networks at the feature level to improve the ECG biometric recognition rate. 2. This research compared the proposed methodology with the transfer learning of pretrained architectures such as [8], ResNet-152 [13] and Inception-V3's [14]. 3. Performed analysis on a combination of all publicly available datasets of NSRDB [4], ECGID [4] and MIT-BIH [5] with a total of 156 subjects, including arrhythmatic patients and healthy controls. This research introduces Deep-Hybrid-ECG, a combination of deep learning and discrete wavelet transform-based approaches for ECG biometric recognition. The biometric identification was performed with a feature-level fusion of one-dimensional convolutional recurrent neural networks (1D-CRNN) and discrete wavelet transform (DWT). The approach has never been employed in previous research studies, to the best of our knowledge. The paper is structured as follows. Section 2 examines the review of the literature, the proposed methodology is presented in Section 3, and Section 4 discusses the experiment's findings. Furthermore, finally, Section 5 wraps up the research with future implications.