Using Convolutional Neural Network and a Single Heartbeat for ECG Biometric Recognition.

Dalal A Alduwaile,Md Saiful Islam

doi:10.3390/e23060733

Abstract

The electrocardiogram (ECG) signal has become a popular biometric modality due to characteristics that make it suitable for developing reliable authentication systems. However, the long segment of signal required for recognition is still one of the limitations of existing ECG biometric recognition methods and affects its acceptability as a biometric modality. This paper investigates how a short segment of an ECG signal can be effectively used for biometric recognition, using deep-learning techniques. A small convolutional neural network (CNN) is designed to achieve better generalization capability by entropy enhancement of a short segment of a heartbeat signal. Additionally, it investigates how various blind and feature-dependent segments with different lengths affect the performance of the recognition system. Experiments were carried out on two databases for performance evaluation that included single and multisession records. In addition, a comparison was made between the performance of the proposed classifier and four well-known CNN models: GoogLeNet, ResNet, MobileNet and EfficientNet. Using a time–frequency domain representation of a short segment of an ECG signal around the R-peak, the proposed model achieved an accuracy of 99.90% for PTB, 98.20% for the ECG-ID mixed-session, and 94.18% for ECG-ID multisession datasets. Using the preprinted ResNet, we obtained 97.28% accuracy for 0.5-second segments around the R-peaks for ECG-ID multisession datasets, outperforming existing methods. It was found that the time–frequency domain representation of a short segment of an ECG signal can be feasible for biometric recognition by achieving better accuracy and acceptability of this modality.

Highlights

The recent explosive evolution in science and technology has raised security standards, rendered classical security methods, such as keys, passwords, PIN codes, and ID cards, unsatisfactory and opened the door for new technologies
This paper investigates how a time–frequency domain representation of a short segment of an ECG signal can be effectively used for biometric recognition, using deep learning techniques to improve the acceptability of this modality
We investigate the effectiveness of time–frequency domain representation of a short segment of an ECG signal (0.5-second window around the R-peak) for improved biometric recognition

Summary

Introduction

The recent explosive evolution in science and technology has raised security standards, rendered classical security methods, such as keys, passwords, PIN codes, and ID cards, unsatisfactory and opened the door for new technologies. Biometric authentication is one approach that provides a unique method for identity recognition This approach uses metrics related to human characteristics, such as facial features [1,2], fingerprints [3,4], hand-geometry [5], handwriting [6,7], the iris [8,9], speech [10,11], and gait [12,13] for identification and verification. These traditional biometric modalities have proved to be vulnerable, as they can be replicated and used fraudulently [14]. Physiological signals, such as electroencephalogram (EEG) signals produced by the brain [15,16] and electrocardiogram (ECG) signals produced by the heart [17,18,19]

Methods

Results

Conclusion