SIGNAL (programming language)

Computer hardware

Random forest

Linguistics

Philosophy

Photoplethysmogram

Statistics

Support vector machine

Computer science

Digital signal processing

Filter (signal processing)

Radiology

Signal processing

Blood pressure

Pattern recognition (psychology)

Mathematics

Kriging

Feature selection

Feature (linguistics)

Artificial intelligence

Medicine

Programming language

Mean squared error

Feature extraction

Machine learning

Computer vision

Preprocessor

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.

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