# Deep Contrastive Learning-Based Model for ECG Biometrics
###### Abstract  
The electrocardiogram (ECG) signal is shown to be promising as a biometric. To this end, it has been demonstrated that the analysis of ECG signals can be considered as a good solution for increasing the biometric security levels. This can be mainly due to its inherent robustness against presentation attacks. In this work, we present a deep contrastive learning-based system for ECG biometric identification. The proposed system consists of three blocks: a feature extraction backbone based on short time Fourier transform (STFT), a contrastive learning network, and a classification network. We evaluated the proposed system on the Heartprint dataset, a new ECG biometrics multi-session dataset. The experimental analysis shows promising capabilities of the proposed method. In particular, it yields an average top1 accuracy of 98.02% on a new dataset built by gathering 1539 ECG records from 199 subjects collected in multiple sessions with an average interval between sessions of 47 days. ECG biometric; biometric identification; contrastive learning; deep learning +
Footnote †: journal: Accepted: 22 February  
identifying the patients by classifying the signal into one of the patients. The beats are detected either manually or automatically by applying [PERSON]'s method [23; 24]. Later, the Residual Depthwise Separable Convolutional Neural Network (RDSCNN) model is applied to classify the signal. Their proposed method achieved an accuracy of 98.89% and 97.92% in the automatic beats detection for ECG-ID and MIT-BIH, respectively. The accuracy enhanced to 100% in the manual beats detection in both datasets. [PERSON] et [PERSON] [25] proposed a deep learning-based model, called BioECG, for ECG biometrics. The proposed model incorporates CNN and LSTM networks for classifying ECG signals. This study was developed and examined on a private dataset that represents potential scenarios in biometric recognition in which data was acquired in different days, in various subject positions, and/or doing different physical activities. [PERSON] et al. [16] proposed a CNN-based approach for ECG biometrics, called Deep-ECG. They evaluated their model on the PTB Diagnostic ECG Database, with identification accuracy of 100%. The Siamese network is a known deep learning architecture that is composed of two or more identical subnetworks. Several studies included Siamese networks for ECG biometric verification [4; 26]. For example, [PERSON] et al. [26] developed a system composed of two Siamese convolutional networks and evaluated it on the ECD-ID database. In their method, the R-peaks are detected from the input ECG and then used for training the Siamese network. Their system achieved an overall accuracy of 86.47%. [PERSON] et al. [4] proposed an ECG biometric system called EDITH, which utilizes a CNN and Siamese architectures. They showed that a single heartbeat is enough for authentication with an accuracy of 96-99.75%. Using multiple beats (3 to 6 beats), they have enhanced the performance to 100%. In their method, the R-peaks are detected from the input ECG signal using a deep learning network (MultiResUNet). Then, the heartbeats are extracted and analyzed further using other deep learning networks. They used a Siamese network to compute the similarity between templates and probe signals, where two identical subnetworks were employed to compare the similarity of two input samples. It is important to note that all previous works were applied and evaluated on medical ECG datasets or on datasets that were captured using medical sensors. Naturally, it would be better to use ECG datasets that are specifically captured for the purpose of biometric authentication. In this paper, we present a new method for ECG biometrics identification based on deep learning techniques. The proposed method is based on the contrastive deep learning approach. Specifically, it is composed of three main blocks: feature extraction, contrastive optimization, and classification.