An automatic optical system for blood pressure measurement based on the pulse transit time and heart rate variability
Part Of
Proceedings of SPIE - The International Society for Optical Engineering
Journal Volume
13437
Start Page
134370L
ISSN
0277786X
ISBN (of the container)
9781510686601
Date Issued
2025
Author(s)
Abstract
In this study, we propose an automatic optical system for continuous blood pressure (BP) monitoring which can calculate pulse transit time (PTT) using photoplethysmography (PPG) and image-photoplethysmography (iPPG) signals, and incorporating key physiological parameters such as blood volume, changes in arterial diameter, and heart rate variability (HRV) to enhance BP measurement accuracy. Given the widespread use of smartphones and headphones, we selected the ear as the measurement site for PPG while using a camera to capture the iPPG signals for the wrist radial artery. Additionally, since the radial artery is at the front of the wrist and images may include other areas beyond this region, we developed an algorithm for automatically identifying the wrist’s region of interest (ROI), replacing the need for manual operation. Then, we utilized a Random Forest model to classify the iPPG signals into three categories and to compute a weighted average to represent the overall iPPG signal of the wrist radial artery. In order to extract waveform features accurately, the ear PPG signal was used as a reference to build a CNN-BiLSTM + Global Context block (GC block) model that enhances the stability of the iPPG waveform. In addition, a features extraction algorithm was implemented for the PPG/iPPG signals, particularly those with less prominent characteristics. Our results demonstrated that this system has the potential to significantly improve personal healthcare, surpassing and as an improvement to traditional cuff-based blood pressure monitors.
Event(s)
Health Monitoring of Structural and Biological Systems XIX 2025, 17 March 2025 - 20 March 2025, Vancouver
Subjects
continuous blood pressure monitoring
heart rate variability
image processing
machine learning
pulse transit time
SDGs
Publisher
SPIE
Type
conference paper