ECG Biometric Authentication: A Comparative Analysis

Abstract

Robust authentication and identification methods become an indispensable urgent task to protect the integrity of the devices and the sensitive data. Passwords have provided access control and authentication, but have shown their inherent vulnerabilities. The speed and convenience factor are what makes biometrics the ideal authentication solution as they could have a low probability of circumvention. To overcome the limitations of the traditional biometric systems, electrocardiogram (ECG) has received the most attention from the biometrics community due to the highly individualized nature of the ECG signals and the fact that they are ubiquitous and difficult to counterfeit. However, one of the main challenges in ECG-based biometric development is the lack of large ECG databases. In this paper, we contribute to creating a new large gallery off-the-person ECG datasets that can provide new opportunities for the ECG biometric research community. We explore the impact of filtering type, segmentation, feature extraction, and health status on ECG biometric by using the evaluation metrics. Our results have shown that our ECG biometric authentication outperforms existing methods lacking the ability to efficiently extract features, filtering, segmentation, and matching. This is evident by obtaining 100% accuracy for PTB, MIT-BHI, CEBSDB, CYBHI, ECG-ID, and in-house ECG-BG database in spite of noisy, unhealthy ECG signals while performing five-fold cross-validation. In addition, an average of 2.11% EER among 1,694 subjects is obtained.

A Comprehensive Survey on ECG Signals as New Biometric Modality for Human Authentication: Recent Advances and Future Challenges

“…The PQ segment depicts the delay in transmission of the impulse at the auriculoventricular node and it is usually zero potential [37], [98].…”

“…The PQ interval depicts the time required to depolarize the auricular musculature plus the delay time encountered at the auriculoventricular node until the beginning of depolarization of the ventricles [37], [98]. Specifically, the PQ-interval originates from the onset of the P-wave to the onset of the QRS-complex.…”

“…The ST segment depicts the duration of the depolarized phase of the ventricles. It is the time between the completion of depolarization and the beginning of repolarization while the chemical processes of depolarization are attempting to reverse themselves [98]. The ST segment is usually zero potential.…”

EMG Biometric Systems Based on Different Wrist-Hand Movements

“…A biometric system is a pattern recognition system that distinguishes individuals or groups from one another by extracting information from biophysical signals [17]. Biometric systems have been demonstrated using fingerprint [13], face [14], voice [15], iris recognition [16], ECG [18]- [20] and/or EMG analysis [21]- [25]. Each offers a different balance of vulnerability, ease of use, obtrusiveness, and complexity.…”

Development of Miniaturized Wearable Wristband Type Surface EMG Measurement System for Biometric Authentication

“…The most promising biometric technology is the use of unique signals, such as electrocardiogram (ECG) and electromyogram (EMG) signals [7,8]. This method analyzes the morphological characteristics of ECG and EMG signals and has a considerable potential for development in the field of biometrics technology because it enables real-time authentication and prevents hacking.…”