Estimating Blood Pressure from the Photoplethysmogram Signal and Demographic Features Using Machine Learning Techniques

Moajjem Hossain Chowdhury,Amith Khandakar,Mamun Bin Ibne Reaz,M Monir Uddin,Md Nazmul Islam Shuzan,Zaid B Mahbub,Muhammad E.H Chowdhury

doi:10.3390/s20113127

Abstract

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.

Highlights

Measuring blood pressure (BP) is an important aspect in monitoring the health of a person.High blood pressure, generally, means that a person has a higher risk of health problems [1]
Out of the 19 algorithms, Gaussian process regression (GPR) and ensemble trees outperformed for all cases in the estimation of both systolic blood pressure and diastolic blood pressure
The feature selected using a combination of ReliefF and GPR performed the best in estimating systolic BP (SBP) while Correlation-based feature selection (CFS) and GPR performed best for diastolic BP (DBP)

Summary

Introduction

Measuring blood pressure (BP) is an important aspect in monitoring the health of a person. Means that a person has a higher risk of health problems [1]. High blood pressure puts a huge amount of strain on the arteries and the heart. This strain can make the arteries less flexible over time. It is important for a person to monitor their blood pressure regularly. In most cases, measuring blood pressure once or twice a day is more than enough. Sometimes the doctor needs to track the blood pressure continuously. This is because blood pressure is known to decrease at night.

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Conclusion