AUTHOR=Hu Xudong , Yin Shimin , Zhang Xizhuang , Menon Carlo , Fang Cheng , Chen Zhencheng , Elgendi Mohamed , Liang Yongbo 

TITLE=Blood pressure stratification using photoplethysmography and light gradient boosting machine

JOURNAL=Frontiers in Physiology

VOLUME=14

YEAR=2023

URL=https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2023.1072273

DOI=10.3389/fphys.2023.1072273

ISSN=1664-042X

ABSTRACT=<p><bold>Introduction:</bold> Globally, hypertension (HT) is a substantial risk factor for cardiovascular disease and mortality; hence, rapid identification and treatment of HT is crucial. In this study, we tested the light gradient boosting machine (LightGBM) machine learning method for blood pressure stratification based on photoplethysmography (PPG), which is used in most wearable devices.</p><p><bold>Methods:</bold> We used 121 records of PPG and arterial blood pressure (ABP) signals from the Medical Information Mart for Intensive Care III public database. PPG, velocity plethysmography, and acceleration plethysmography were used to estimate blood pressure; the ABP signals were used to determine the blood pressure stratification categories. Seven feature sets were established and used to train the Optuna-tuned LightGBM model. Three trials compared normotension (NT) vs. prehypertension (PHT), NT vs. HT, and NT + PHT vs. HT.</p><p><bold>Results:</bold> The F1 scores for these three classification trials were 90.18%, 97.51%, and 92.77%, respectively. The results showed that combining multiple features from PPG and its derivative led to a more accurate classification of HT classes than using features from only the PPG signal.</p><p><bold>Discussion:</bold> The proposed method showed high accuracy in stratifying HT risks, providing a noninvasive, rapid, and robust method for the early detection of HT, with promising applications in the field of wearable cuffless blood pressure measurement.</p>