Open AccessOptical blood pressure estimation with photoplethysmography and FFT-based neural networks
Author(s) -
Xiaopeng Xing,
Mengtao Sun
Publication year - 2016
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.7.003007
Subject(s) - photoplethysmogram , fast fourier transform , computer science , normalization (sociology) , artificial intelligence , artificial neural network , beat (acoustics) , pattern recognition (psychology) , blood pressure , signal processing , signal (programming language) , speech recognition , computer vision , acoustics , algorithm , digital signal processing , medicine , physics , filter (signal processing) , sociology , anthropology , computer hardware , radiology , programming language
We introduce and validate a beat-to-beat optical blood pressure (BP) estimation paradigm using only photoplethysmogram (PPG) signal from finger tips. The scheme determines subject-specific contribution to PPG signal and removes most of its influence by proper normalization. Key features such as amplitudes and phases of cardiac components were extracted by a fast Fourier transform and were used to train an artificial neural network, which was then used to estimate BP from PPG. Validation was done on 69 patients from the MIMIC II database plus 23 volunteers. All estimations showed a good correlation with the reference values. This method is fast and robust, and can potentially be used to perform pulse wave analysis in addition to BP estimation.