A Multistage Signal Quality Framework for Blood Pressure Monitoring Using Photoplethysmography

Cuffless Blood Pressure (BP) monitoring using photoplethysmography (PPG) is attractive for continuous wearable assessment, however, it is highly susceptible to motion artifacts and poor signal quality. We propose a simple and interpretable multi-stage signal-quality (SQ) framework that embeds three sequential quality checks directly into the PPG → BP estimation pipeline: (1) raw-signal metrics (signal jitters, zero crossings), (2) beat-level plausibility (peak-to-peak amplitude and inter-beatinterval changes), and (3) multi-sensor consistency (pulse arrival time variability). Each detected pulse is labelled good / bad, aggregated over a one-minute window, and gated by a tunable Good Signal Threshold (GST). GST was calibrated on a small test set, and GST =0.7 was chosen because it retained 99 % of stationary windows while rejecting 90 % of motion-contaminated windows. In a feasibility study with subjects undergoing oneday measurements of 24 hours, PPG-derived heart rate (HR) was compared against ECG ground truth. Ablation analysis showed incremental benefits from each SQ stages: during sleep, error reduced from 2.08 bpm to 1.22 bpm with 80 % coverage; while during daytime activity, error decreased from 3.94 bpm to 1.69 bpm, albeit with reduced coverage of 50 %. These results confirm that the SQ framework substantially improves pulse fidelity, achieving 40-55% error reduction, while maintaining interpretable design and low computational cost. The proposed approach is thus well-suited for low-power wearable systems and provides a practical foundation for cuffless BP monitoring.