Optical blood pressure estimation with photoplethysmography and FFT-based neural networks

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 ampl...

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Bibliographic Details
Published inBiomedical optics express Vol. 7; no. 8; pp. 3007 - 3020
Main Authors Xing, Xiaoman, Sun, Mingshan
Format Journal Article
LanguageEnglish
Published United States Optical Society of America 01.08.2016
Subjects
(170.1610) Clinical applications
(170.3890) Medical optics instrumentation
(280.1415) Biological sensing and sensors
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ISSN2156-7085
2156-7085
DOI10.1364/BOE.7.003007

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Summary: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.
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ISSN:2156-7085
2156-7085
DOI:10.1364/BOE.7.003007