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Exploratory data analysis of tonometry and PTT/PIR signals for cuffless blood pressure monitoring: dynamic
Ye Yang1, Qiao Li2, Mohamed Almadi3
1Neural Systems Group, Massachusetts General Hospital, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, 02129, United States.
Objective:
Sensing modalities for cuffless blood pressure (BP) monitoring remain insufficiently characterized. To address this, we used exploratory data analysis (EDA) to evaluate multimodal physiological signals' associations with reference BP, to gain insight into their BP-related information and characteristics.
Approach:
Time-synchronized electrocardiogram (ECG), photoplethysmography (PPG), and superficial temporal artery tonometry (STAT) signals were collected from 28 sessions across 9 healthy participants during resting and handgrip-induced BP perturbations, referenced against continuous Finapres BP. Pulse transit time (PTT)/PPG intensity ratio (PIR)-derived and STAT-derived BP estimates were evaluated via participant-level Pearson correlation and φk coefficients, dynamic-responsiveness/baseline-stability measures, and a phase-resolved (stable vs. dynamic) analysis.
Main Results:
PTT/PIR-derived estimates showed weak linear association with reference SBP (r ≈ 0.37-0.41) and weaker association with DBP, with nonlinear association (φk ≈ 0.58-0.62 SBP, 0.38-0.57 DBP) exceeding their linear correlation. STAT-derived estimates showed very weak linear correlation but the strongest nonlinear association (φk ≈ 0.68 SBP, 0.71 DBP) - modest for SBP but more pronounced for DBP. Phase-resolved analysis confirmed STAT tracked BP more closely during the dynamic (handgrip) phase and PTT/PIR during the stable phase, a genuine, phase-specific pattern rather than a pooling artifact; because Finapres can amplify beat-to-beat amplitude, this should be read relative to that reference, not invasive BP.
Significance:
These complementary properties motivate a fusion strategy using STAT to track rapid BP changes while PTT/PIR anchors the baseline. More broadly, this EDA-derived understanding can guide more data-efficient, interpretable models - properties clinicians require regardless of data volume. Given the modest, healthy-only sample, findings should be regarded as preliminary.
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