###### Abstract  
The intrinsic and liveness detection behavior of electrocardiogram (ECG) signals has made it an emerging biometric modality for the researcher with several applications including forensic, surveillance and security. The main challenge is the low recognition performance with datasets of large populations, including healthy and heart-disease patients, with a short interval of an ECG signal. This research proposes a novel method with the feature-level fusion of the discrete wavelet transform and a one-dimensional convolutional recurrent neural network (1D-CRNN). ECG signals were preprocessed by removing high-frequency powerline interference, followed by a low-pass filter with a cutoff frequency of 1.5 Hz for physiological noises and by baseline drift removal. The preprocessed signal is segmented with PQRST peaks, while the segmented signals are passed through Coiflets' 5 Discrete Wavelet Transform for conventional feature extraction. The 1D-CRNN with two long short-term memory (LSTM) layers followed by three 1D convolutional layers was applied for deep learning-based feature extraction. These combinations of features result in biometric recognition accuracies of 80.64%, 98.81% and 99.62% for the ECG-ID, MIT-BIH and NSR-DB datasets, respectively. At the same time, 98.24% is achieved when combining all of these datasets. This research also compares conventional feature extraction, deep learning-based feature extraction and a combination of these for performance enhancement, compared to transfer learning approaches such as VGG-19, ResNet-152 and Inception-v3 with a small segment of ECG data. ECG biometric recognition; deep learning; transfer learning; 1D convolutional recurrent neural network; DWT features 202323234635  
about identification privacy or remembering complex passwords. Biometric technology can therefore be used to address the increasing demand for global security. Measurements refer to measures, while bio relates to life. These constitute an automatic method for identifying an individual utilizing particular biometrics. They could be classified into two categories based on such traits: physiological character traits and behavioral character traits [2]. A person's gait or walking style, keystrokes, signatures, as well as speech patterns, among several others, are based on behavioral biometrics. These traits depend on the way a person behaves. Similarly, physiological traits belong to the physical structure and are intrinsic to the human body. This category includes body parts such as the ear shape, hand geometry, iris, face and fingerprint. Medical biometric technology is a part of physiological traits that include physical properties associated with the medical field and are particularly useful in patient identification and data integrity in the medical industry. Heartbeats, DNA, and other biological features are included. Electrophysiological signals such as ECG and EEG are examples of medical biometrics. Electrocardiograms can provide a more reliable biometric solution, even though they fulfill the majority of the requirements for an ideal biometric, especially when compared to other biometrics [1]. Because of the distinct variations in the morphology or structure of hearts, each person's ECG signal has a unique pattern. Even twin brothers who have the same iris structure or facial features would have different ECG readings [1]. ECG offers the advantage of an innate property of liveness detection, which sets it apart from other biometric techniques. Liveness detection ensures that the individual providing the biometric is genuinely present and carrying it. It can significantly reduce the likelihood of fraud or other spoofing attacks on the biometric system [1]. Identifying individuals using medical biometrics is a critical task. In arrhythmia patients, for example, the flow of the heartbeat is irregular when using ECG signals. The impact of arrhythmia on an individual will be discussed in later sections.