# A PRISMA-based systematic review on advances in identity recognition and authentication using human biometric signals (2018–2023)

## TL;DR

This review analyzes how well biometric signals like ECG, EEG, and PPG can be used for identity authentication, finding that ECG and multimodal systems with deep learning perform best.

## Contribution

The paper provides a systematic review of biometric authentication performance from 2018–2023, highlighting the superiority of ECG and multimodal deep learning approaches.

## Key findings

- ECG-based authentication systems achieved an average accuracy of 98.6%, the highest among biometric modalities.
- Multimodal biometric systems showed accuracy exceeding 99%, outperforming unimodal systems in reliability and robustness.
- Deep learning methods outperformed traditional machine learning, with accuracy rates up to 99.8% on specific datasets.

## Abstract

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.

The performance of biometric signal-based authentication systems was analyzed. The study examined how biometric signals such as ECG, EEG, and PPG are used in authentication systems.The highest accuracy rate was achieved with ECG-based systems. ECG-based systems were found to be the most reliable biometric method, with an average accuracy rate of 98.6%.Deep learning models achieved accuracy rates up to 99.3% (EEGMMIDB dataset) and 99.8% (ECG PTB-XL dataset), consistently outperforming traditional machine learning methods (typically 95–97%).Multimodal biometric systems were found to be more reliable than unimodal systems. Mul timodal biometric systems were considered more robust and resistant to attacks than single signal-based methods.Larger datasets and advanced signal processing techniques were associated with improved accuracy. The size of the dataset, signal processing methods, and classification algorithms directly impacted the performance of biometric authentication

The performance of biometric signal-based authentication systems was analyzed. The study examined how biometric signals such as ECG, EEG, and PPG are used in authentication systems.

The highest accuracy rate was achieved with ECG-based systems. ECG-based systems were found to be the most reliable biometric method, with an average accuracy rate of 98.6%.

Deep learning models achieved accuracy rates up to 99.3% (EEGMMIDB dataset) and 99.8% (ECG PTB-XL dataset), consistently outperforming traditional machine learning methods (typically 95–97%).

Multimodal biometric systems were found to be more reliable than unimodal systems. Mul timodal biometric systems were considered more robust and resistant to attacks than single signal-based methods.

Larger datasets and advanced signal processing techniques were associated with improved accuracy. The size of the dataset, signal processing methods, and classification algorithms directly impacted the performance of biometric authentication