Key result
Multimodal ECG authentication systems achieve ~99% accuracy, outperforming traditional methods via deep learning.
Why the study?
To examine the effectiveness of physiological biometric signals (e.g., ECG, EEG, PPG) in authentication and recognition systems, evaluating signal processing, classification approaches, and multimodal versus unimodal performance.
Physiological biometric signals, particularly ECG and multimodal approaches using deep learning, provide highly accurate (>98%) human identification and authentication.
ECG biometrics merit human validation trials; leaves open translation from animal models to clinical authentication.
This systematic review examines the effectiveness of physiological biometric signals in authentication and recognition systems by analyzing studies published between 2018 and 2023. Specifically, different biometric modalities (e.g., ECG, EEG, and PPG), commonly used datasets, signal processing techniques, and classification approaches are evaluated to assess their reported reliability and performance. In addition, the performance of multimodal biometric systems is compared with that of unimodal approaches. The review was conducted in accordance with the PRISMA 2020 guidelines. Relevant studies published between 2018 and 2023 were systematically retrieved from major databases, including EBSCO, PubMed, IEEE Xplore, Scopus, and Web of Science. A total of 2,064 records were initially identified, and after duplicate removal and eligibility screening, 80 articles were included in the final review. The study selection process is summarized using a PRISMA flow diagram. The reviewed studies indicate that ECG-based authentication systems report high average accuracy (98.6%), while multimodal biometric systems generally achieve accuracy levels exceeding 99%. Across modalities, deep learning–based approaches tend to outperform traditional machine learning methods. Dataset size and the choice of signal processing techniques were also found to influence reported performance outcomes. Overall, the findings suggest that biometric signal–based authentication systems demonstrate strong performance under the evaluation conditions reported in the literature. Multimodal fusion and deep learning approaches appear particularly promising, although reported results vary across datasets and protocols. Future research should prioritize larger and more diverse datasets, standardized evaluation benchmarks, and optimized signal processing pipelines to improve comparability and real-world applicability. Further studies on the integration of complementary biometric signals are also warranted.
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Çokçetin et al. (2026) studied this question. ECG-based authentication systems achieved 98.6% accuracy, while multimodal systems exceeded 99% accuracy, highlighting superior performance of deep learning methods.
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