Key result
Deep learning framework EDITH reduces ECG biometric verification error to ~1% using a single heartbeat.
Why the study?
Physiological signal-based authentication using ECG shows promise against forgery, but there is a need to develop reliable deep learning frameworks using fewer heartbeats.
A deep learning-based framework using Siamese architectures (EDITH) provides highly accurate individual authentication using single or multiple ECG heartbeats.
Answers are grounded in this paper and cite their sources.
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Supports ECG authentication feasibility; leaves open prospective clinical validation before adoption.
In recent years, physiological signal-based authentication has shown great promises, for its inherent robustness against forgery. Electrocardiogram (ECG) signal, being the most widely studied biosignal, has also received the highest level of attention in this regard. It has been proven with numerous studies that by analyzing ECG signals from different persons, it is possible to identify them, with acceptable accuracy. In this work, we present, EDITH, a deep learning-based framework for ECG biometrics authentication system. Moreover, we hypothesize and demonstrate that Siamese architectures can be used over typical distance metrics for improved performance. We have evaluated EDITH using 4 commonly used datasets and outperformed the prior works using a fewer number of beats. EDITH performs competitively using just a single heartbeat (96 <tex-math notation="LaTeX">~</tex-math> 99.75% accuracy) and can be further enhanced by fusing multiple beats (100% accuracy from 3 to 6 beats). Furthermore, the proposed Siamese architecture manages to reduce the identity verification Equal Error Rate (EER) to 1.29 %. A limited case study of EDITH with real-world experimental data also suggests its potential as a practical authentication system.
Ibtehaz et al. (2021) studied Healthy individuals and patients with arrhythmia. EDITH (ECG biometrics aided by Deep learning) vs. Prior ECG biometric authentication methods was evaluated on Identity verification Equal Error Rate (EER). EDITH, a deep learning-based framework for ECG biometrics, achieved 96-99.75% accuracy using a single heartbeat and reduced the identity verification Equal Error Rate to 1.29%.