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Interpretable evaluation of physiological signals for biometric identification.
Vithurabiman Senthuran1, Uthayasanker Thayasivam1, Iynkaran Natgunanathan2
1Department of Computer Science and Engineering, University of Moratuwa, Moratuwa, 10400, Sri Lanka.
Computers in Biology and Medicine
|March 11, 2026
Summary
This study introduces a framework to analyze biometric systems using physiological signals like ECG, EEG, and PPG. It reveals key signal features and factors influencing performance for secure, real-world applications.
Area of Science:
- Biometrics
- Signal Processing
- Machine Learning
Background:
- Biometric systems using physiological signals achieve high identification accuracy (IA) and low Equal Error Rate (EER).
- Existing research often prioritizes performance metrics over model characteristics.
- Understanding how signal features, population size, and sample availability impact biometric system reliability is crucial.
Purpose of the Study:
- To develop a unified framework for analyzing ECG, EEG, and PPG signals in biometric systems.
- To investigate the influence of signal features, population size, and sample availability on biometric performance and reliability.
- To enhance the interpretability and inform the development of biometric systems for real-world deployment.
Main Methods:
- Proposed a unified framework to analyze ECG, EEG, and PPG signals.
- Employed machine learning models for performance assessment and interpretability.
- Utilized SHapley Additive exPlanations (SHAP) to quantify feature importance.
- Conducted sensitivity analyses on population size and sample availability.
Main Results:
- Achieved identification accuracy (IA) consistently exceeding 96% across most datasets.
- Demonstrated lower Equal Error Rate (EER) compared to existing deep learning-based studies.
- SHAP analysis identified wavelet coefficients and PPG systolic features as highly discriminative.
- Sensitivity analyses provided insights into performance variations with population size and sample availability.
Conclusions:
- The proposed framework enables a deeper understanding of biometric system behavior beyond traditional metrics.
- Identified key discriminative features and influencing factors for robust biometric system design.
- Supports the development of secure, privacy-aware biometric systems for diverse deployment scenarios, including resource-constrained edge environments.
