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Hypertension is a potentially unsafe health ailment, which can be indicateddirectly from the Blood pressure (BP). Hypertension always leads to otherhealth complications. Continuous monitoring of BP is very important; however,cuff-based BP measurements are discrete and uncomfortable to the user. Toaddress this need, a cuff-less, continuous and a non-invasive BP measurementsystem is proposed using Photoplethysmogram (PPG) signal and demographicfeatures using machine learning (ML) algorithms. PPG signals were acquired from219 subjects, which undergo pre-processing and feature extraction steps. Time,frequency and time-frequency domain features were extracted from the PPG andtheir derivative signals. Feature selection techniques were used to reduce thecomputational complexity and to decrease the chance of over-fitting the MLalgorithms. 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 withReliefF feature selection algorithm outperforms other algorithms in estimatingSBP 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 BPand avoid any critical health conditions due to sudden changes.

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