Robust synchronization of cardiac-linked biosensors under peak detection failures: a modality-agnostic approach via structural repair and Huber-IRLS regression
Year: 2026
Type: article
Abstract: Cooperative sensing in clinical instruments, distributed medical devices, and body-area networks requires accurate relative timing between independently clocked sensors, such as electrocardiography (ECG) and photoplethysmography (PPG). Interval-only synchronization operates directly on inter-beat intervals and avoids waveform alignment, but conventional estimators generally assume one-to-one reference–device beat correspondence. Missed beats, motion-induced false peaks, and gross interval errors violate this assumption by creating merged or split intervals and index shifts. We propose a robust interval-only synchronization pipeline comprising: (i) Dynamic Repair of abnormally short split intervals caused by extra (false) peaks; (ii) greedy peak-failure-aware mapping with q ∈ { 1 , 2 } to represent isolated missed beats as merged intervals; and (iii) Huber-iteratively reweighted least squares regression with median absolute deviation-based residual gating. Approximate uncertainty is quantified using an OLS-style Newey–West HAC covariance estimate (approximate and conditional when gating) and Fieller confidence sets for the ratio-defined delay-dynamics parameter. Monte Carlo ablation on synthetic ECG/PPG data (ORACLE and SIGNAL pipelines) shows that structural peak-detection failures can be catastrophic for OLS (e.g. RMS E after , all ≈ 227 ms under Extra 10% and ≈ 232 ms under a mixed extreme regime). In contrast, the proposed pipeline maintains robustness; in the mixed extreme regime it achieves RMS E after , in ≈ 37.4 ms (SIGNAL) and ≈35.9 ms (ORACLE). The real-data experiment was performed as a semi-synthetic feasibility validation using matched ECG/PPG beats from a real recording, with known clock skew imposed on the PPG timestamps and controlled peak failures injected into the resulting device interval sequence.
Author: Khaled Ben Abdessalem
Institutions: University of Kairouan, Tunis El Manar University
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,325
OpenAlex ID: W7225426450