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

• URL: http://arxiv.org/abs/2005.03357v1
• Date: Thu, 7 May 2020 09:45:02 GMT
• Title: Estimating Blood Pressure from Photoplethysmogram Signal and Demographic Features using Machine Learning Techniques
• Authors: Moajjem Hossain Chowdhury, Md Nazmul Islam Shuzan, Muhammad E.H. Chowdhury, Zaid B Mahbub, M. Monir Uddin, Amith Khandakar, Mamun Bin Ibne Reaz
• Abstract summary: Hypertension is a potentially unsafe health ailment, which can be indicated directly from the Blood pressure (BP) Continuous monitoring of BP is very important; however, BP measurements are discrete and uncomfortable to the user. To address this need, a cuffless, continuous and a non-invasive BP measurement system is proposed.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 a non-invasive BP measurement system is proposed using Photoplethysmogram (PPG) signal and demographic features using machine learning (ML) algorithms. PPG signals were acquired from 219 subjects, which undergo pre-processing 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 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.

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