Design and HDL Implementation of Pulse-Arrival-Time Estimation Using XGBoost Regression With Tree-Recycling Architecture

• Published in: IEEE access Vol. 13; pp. 27596 - 27609
• Main Authors: Choi, Hyo-Jeong, Um, Ji-Yong
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Biomedical monitoring; Blood pressure; Boosting; Co-design; Comparative studies; Computer architecture; decision tree ensemble model; Decision trees; Design for recycling; Elasticity; Estimation; extreme gradient boosting regression; field programmable gate array; Field programmable gate arrays; Hardware; hardware description language; Hardware description languages; Hardware design languages; Integrated circuits; Parameter estimation; Pulse arrival time; Real time; Regression analysis; Regression models; Regression tree analysis; Software; Training; tree-recycling architecture; Wave velocity; Waveforms
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2025.3540440

This paper presents a pulse-arrival-time (PAT) estimation scheme using Extreme Gradient Boosting (XGBoost) regression and its implementation with hardware description language (HDL). PAT is a significant metric for the estimation of pulse wave velocity (PWV) and blood pressure. We leverage morphological features of single photoplethysmogram (PPG) waveforms and subject-specific parameters to estimate PAT values through a decision tree ensemble regression model. XGBoost regression is adopted for PAT estimation through a comparative study conducted in software. To enable the integration of the regression model into a single chip, we propose a tree-recycling architecture for an area-efficient implementation of the decision tree ensemble model. The proposed PAT estimation has been implemented on a field-programmable gate array (FPGA, Artix7, Xilinx) for real-time verification using a dataset. Additionally, the active area of the proposed scheme in an integrated circuit was estimated after the place-and-route (PnR) procedure, resulting in an area of 1.44 mm2. We utilized the Vital Signs DataBase (VitalDB) for training and testing the proposed PAT estimation in a software-hardware co-design framework. Validation on 80 subjects yielded a determination coefficient (R2) of 0.983 in the linear regression analysis after implementing the HDL on an FPGA.