# Non-Invasive Blood Pressure Estimation Enhanced by Capillary Refill Time Modulation of PPG Signals

## TL;DR

This study shows that using capillary refill time to modify PPG signals improves the accuracy of non-invasive blood pressure monitoring.

## Contribution

The novel use of CRT modulation in PPG signals significantly enhances machine learning-based CBP estimation accuracy.

## Key findings

- CRT modulation reduced MAE by up to 35.6% and MAPE by up to 40.6% for ResNetCNN models.
- All models met AAMI criteria for accuracy in blood pressure estimation.
- The method shows promise for improving wearable CBP monitoring in resource-limited settings.

## Abstract

This study evaluates the impact of capillary refill time (CRT) modulation on photoplethysmography (PPG) signals for improved non-invasive continuous blood pressure (CBP) estimation. Data from 21 healthy participants were collected, applying a standardized 9 N pressure for 15 s to induce CRT during 6-min sessions. PPG signals were segmented into 252 paired 30-s intervals (CRT-modulated and standard). Three machine learning models—ResNetCNN, LSTM, and Transformer—were validated using leave-one-subject-out (LOSO) and non-LOSO methods. CRT modulation significantly enhanced accuracy across all models. ResNetCNN showed substantial improvements, reducing mean absolute error (MAE) by up to 35.6% and mean absolute percentage error (MAPE) by up to 40.6%. LSTM and Transformer models also achieved notable accuracy gains. All models met the Association for the Advancement of Medical Instrumentation (AAMI) criteria (mean error < 5 mmHg; standard deviation < 8 mmHg). The findings suggest CRT modulation’s strong potential to improve wearable CBP monitoring, especially in resource-limited settings.