Continuous blood pressure monitoring via hemodynamic parameter and pulse transit time derived from capacitive sensing pads

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Strong correlation r≥0.94 with arm-cuff reference, r≥0.89 with continuous finger-cuff

• Primary Outcome: Blood pressure estimation accuracy compared to reference standards

• Nnt Or Sensitivity: Mean error ≤0.1 mmHg (arm-cuff) and ≤0.2 mmHg (finger-cuff) with SD ≤5.6 mmHg and ≤7.8 mmHg respectively

• Confidence Interval: Not reported

Clinical Application

Requires specialized capacitive sensing equipment and neural network processing; unclear cost-effectiveness and maintenance requirements Would require TGA approval as medical device; potential integration with existing sleep study protocols in Australian sleep centres; alignment with RACGP hypertension management guidelines for 24-hour monitoring Currently limited to healthy adults in controlled supine positions; requires validation in hypertensive patients and sleep disorder populations

Abstract

Objective.Continuous blood pressure (BP) monitoring is crucial for detecting nocturnal hypertension and acute hemodynamic changes. Conventional cuff-based methods disrupt sleep and miss transient BP fluctuations from sleep-related events or instability. Cuffless methods, such as pulse transit time (PTT), offer potential but often struggle to reliably track acute BP fluctuations due to complex and nonlinear hemodynamics.Approach.We developed a capacitive sensing pad system incorporating PTT with additional hemodynamic features for unobtrusive, continuous BP monitoring in supine subjects. The pad contains ultra-sensitive single-electrode capacitive (SEC) sensors made from carbon nanotube composites. In passive contact, SEC sensor on the back of the chest captures intrathoracic blood volume changes through deep tissue permittivity and ballistocardiography, while another under the leg detects arterial pulse-induced tissue vibrations. PTT is derived from the temporal delay between chest and leg signals. A neural network model incorporates PTT and intrathoracic blood volume features to improve BP estimation.Main results.In human trials (N= 30) with arm-cuff reference, the system showed strong correlation (r⩾ 0.94), with a mean error (ME) ⩽ 0.1 mmHg and standard deviation (SD) ⩽ 5.6 mmHg. In a subset (N= 8) with continuous finger-cuff reference, it maintained strong correlation (r⩾ 0.89), with a ME ⩽ 0.2 mmHg and SD ⩽ 7.8 mmHg.Significance.These results suggest the possibility of bed-based unobtrusive and continuous BP monitoring in the supine position.

References

1. 1.Cheng, Y. J., Kim, E., Hahn, J. O., Chung, J. H., & Kwon, Y. (2026). Continuous blood pressure monitoring via hemodynamic parameter and pulse transit time derived from capacitive sensing pads. Physiological Measurement. https://doi.org/10.1088/1361-6579/ae575a