Signal Quality and Cross-Session Robustness in ECG Biometric Authentication: A Review of Methods and Challenges

Q: What is the clinical evidence from this study?

Study design: Review. Population: ECG biometric authentication. Intervention: Quality-aware ECG signal processing and learned representations (1D CNN) vs. Conventional handcrafted feature-based baseline (SVM, random forest) without explicit quality rejection. Primary outcome: Biometric verification performance (false acceptance rate, false rejection rate, equal error rate, and ROC-AUC).

Q: What does this research mean for the field?

A quality-aware ECG signal processing and cross-session evaluation protocol is required to reliably evaluate biometric verification performance across FAR, FRR, and EER metrics. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Dhanashree Kamble; Akanksha Hegade

doi:10.55041/ijsrem68623

October 1, 2026INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENTOpen Access

Signal Quality and Cross-Session Robustness in ECG Biometric Authentication: A Review of Methods and Challenges

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Key result

Quality-aware ECG preprocessing framework is proposed for biometric verification without reported experimental results.

Why the study?

Practical performance of ECG-based biometric authentication depends on acquisition conditions, preprocessing, segmentation, feature representation, and evaluation protocols, requiring particular attention to signal quality and cross-session variability.

Key Points

To propose a reproducible study design and signal processing pipeline for ECG-based biometric verification that accounts for signal quality degradation and cross-session variability.
Outlined a staged pipeline advancing from baseline handcrafted features and conventional classifiers (support vector machines and random forests) to quality-aware processing and 1D convolutional neural networks.
Defined a verification protocol separating enrollment and verification recordings to evaluate false acceptance rate, false rejection rate, equal error rate, and receiver operating characteristic curves.
Identified multi-session candidate datasets, including the 90-volunteer ECG-ID database, requiring subject- and session-aware splits rather than correlated beat-level random partitioning.
No empirical experimental results or classifier benchmark metrics are claimed in this study design draft.
The methodological analysis demonstrates that random beat-level splitting inflates apparent authentication performance by placing highly correlated cardiac cycles into both training and testing sets.
The proposed framework shows that explicit signal-quality assessment must balance error reduction on clean segments against the practical operational cost of rejecting genuine user attempts.

Structured PICO

P

Population

Public ECG databases, including the ECG-ID database (a candidate public resource with recordings from 90 volunteers)

I

Intervention

Staged ECG biometric verification pipeline with explicit signal-quality handling, carefully selected preprocessing (band-pass/baseline correction, R-peak detection, beat segmentation), and optional learned 1D CNN representations

C

Comparator

Conventional baseline using a compact set of handcrafted temporal and morphological features with a Support Vector Machine (SVM) or random forest classifier, omitting explicit quality rejection

O

Outcome

Biometric verification performance measured by false acceptance rate (FAR), false rejection rate (FRR), equal error rate (EER), and receiver operating characteristic area under the curve (ROC-AUC)

This review and study design outlines a reproducible framework for evaluating how signal quality assessment and cross-session protocols impact ECG-based biometric authentication.

Limitations

No experimental results are claimed or reported in this draft.
Limited size and acquisition conditions of public ECG datasets such as ECG-ID.
Differences between clinical and biometric collection protocols.
Possible changes in physiology over time affecting cross-session robustness.
Limited generalizability of results from a single database.
Public benchmark results alone cannot establish security against spoofing or presentation attacks.
Size and acquisition conditions of public ECG datasets
Differences between clinical and biometric collection protocols
Possible changes in physiology over time
Limited generalizability of results from a single database
No experimental results are claimed in this draft

Ask about this paper

Answers are grounded in this paper and cite their sources.

Implication

Proposed ECG biometric protocols should not yet change practice; leaves open whether quality-aware cross-session evaluation improves verification metrics.

Abstract

Electrocardiogram (ECG)-based biometric authentication uses physiological signals to verify a claimed identity. Its practical performance depends not only on the classifier but also on acquisition conditions, preprocessing, segmentation, feature representation, and the protocol used to evaluate genuine and impostor attempts. This paper presents a proposed study of ECG signal processing for biometric verification, with particular attention to signal quality and cross-session variability. A staged pipeline is outlined, beginning with a conventional feature-based baseline and progressing to quality-aware processing and, where data permit, learned representations. The proposed evaluation separates enrollment and verification data and emphasizes false acceptance rate, false rejection rate, equal error rate, and receiver operating characteristic analysis. Public ECG databases, including ECG-ID, are considered as candidate resources, subject to their suitability for the final protocol. No experimental results are claimed in this draft; the contribution is a reproducible study design to be refined after a focused review and implementation .

Discussion

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Cite This Study

Kamble et al. (2026) conducted a review in ECG biometric authentication. Quality-aware ECG signal processing and learned representations (1D CNN) vs. Conventional handcrafted feature-based baseline (SVM, random forest) without explicit quality rejection was evaluated on Biometric verification performance (false acceptance rate, false rejection rate, equal error rate, and ROC-AUC). Quality-aware ECG signal processing and cross-session evaluation protocols are proposed to evaluate biometric verification via FAR, FRR, and EER, with no experimental results reported.

synapsesocial.com/papers/6af0dba03c845ca7837ceda9 https://doi.org/10.55041/ijsrem68623

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Signal Quality and Cross-Session Robustness in ECG Biometric Authentication: A Review of Methods and Challenges

Key Points

Structured PICO

Limitations

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

Signal Quality and Cross-Session Robustness in ECG Biometric Authentication: A Review of Methods and Challenges

Key Points

Structured PICO

Limitations

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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