Improved PPG-based estimation of the blood pressure using latent space features

Accurate and uninterrupted estimation of the blood pressure is essential for continuous monitoring of patients. We estimate the blood pressure by extracting 21 time parameters from the photoplethysmography signal. The major novelties of this paper include: (1) using a nonlinear mapping to reduce the size of the feature vector and to map the input parameters to a latent space instead of conventional dimensionality reduction schemes, (2) employing a multi-stage noise reduction technique to effectively smooth the input signal. Estimation of the blood pressures is performed by a support vector regressor. The mean absolute errors of our results are 1.21 mmHg and 0.80 mmHg for systolic and diastolic blood pressures, respectively, which are lower than recent researches.