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Uncertainty in Blood Pressure Measurement Estimated Using Ensemble-Based Recursive Methodology
Open AccessArticle

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

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Department of Electrical and Computer Engineering, North South University, Dhaka 1229, Bangladesh
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Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
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Department of Mathematics and Physics, North South University, Dhaka 1229, Bangladesh
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Department of Electrical, Electronic & Systems Engineering, Universiti Kebangsaan Malaysia, Bangi Selangor 43600, Malaysia
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Author to whom correspondence should be addressed.
Sensors 2020, 20(11), 3127; https://doi.org/10.3390/s20113127
Received: 15 March 2020 / Revised: 6 May 2020 / Accepted: 7 May 2020 / Published: 1 June 2020
(This article belongs to the Special Issue Artificial Intelligence in Medical Sensors)
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. View Full-Text
Keywords: blood pressure; photoplethysmograph; feature selection algorithm; machine learning blood pressure; photoplethysmograph; feature selection algorithm; machine learning
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Chowdhury, M.H.; Shuzan, M.N.I.; Chowdhury, M.E.; Mahbub, Z.B.; Uddin, M.M.; Khandakar, A.; Reaz, M.B.I. Estimating Blood Pressure from the Photoplethysmogram Signal and Demographic Features Using Machine Learning Techniques. Sensors 2020, 20, 3127.

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