2020/05/07 by Moajjem Hossain Chowdhury, Md Nazmul Islam Shuzan, Muhammad E. H. Chowdhury +6
Computer Science · Engineering · Medicine · Psychology · #Blood pressure #Feature (linguistics) #Feature extraction #Feature selection #Mean squared error #Non-Invasive Vital Sign Monitoring #Optical Imaging and Spectroscopy Techniques #Pattern recognition (psychology) #Photoplethysmogram #Preprocessor #SIGNAL (programming language) #Sleep and Work-Related Fatigue #cs.LG #eess.SP
paper · pdf · doi:10.3390/s20113127
published as Sensors 2020, 20(11), 3127 · Accepted for publication in Sensor, 14 Figures, 14 Tables
arxiv created 2020/05/07 · openalex publication_date 2020/06/01 · arxiv updated 2020/06/04 · openalex created_date 2020/06/05 · openalex updated_date 2026/08/06
Hypertension is a potentially unsafe health ailment, which can be indicated directly from the blood pressure (BP). Hypertension always leads to other health complications. Continuous monitoring of BP is very important; however, cuff-based BP measurements are discrete and uncomfortable to the user. To address this need, a cuff-less, continuous, and noninvasive BP measurement system is proposed using the photoplethysmograph (PPG) signal and demographic features using machine learning (ML) algorithms. PPG signals were acquired from 219 subjects, which undergo preprocessing and feature extraction steps. Time, frequency, and time-frequency domain features were extracted from the PPG and their derivative signals. Feature selection techniques were used to reduce the computational complexity and to decrease the chance of over-fitting the ML algorithms. The features were then used to train and evaluate ML algorithms. The best regression models were selected for systolic BP (SBP) and diastolic BP (DBP) estimation individually. Gaussian process regression (GPR) along with the ReliefF feature selection algorithm outperforms other algorithms in estimating SBP and DBP with a root mean square error (RMSE) of 6.74 and 3.59, respectively. This ML model can be implemented in hardware systems to continuously monitor BP and avoid any critical health conditions due to sudden changes.