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2018 | OriginalPaper | Buchkapitel

Noninvasive Blood Pressure Measurement Based on Photoplethysmography

verfasst von : Yudong Wu, Shuncong Zhong

Erschienen in: Advanced Mechanical Science and Technology for the Industrial Revolution 4.0

Verlag: Springer Singapore

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Abstract

Stationary wavelet transform (SWT) and the Artificial Neural Networks (ANN) Algorithm were proposed for continuous measurement of blood pressure. A total number of 26,900 photoplethysmography (PPG) signals from Mimic Database were analyzed using SWT decomposition. The fifth layer high frequency coefficient was employed to reconstruct the signals. Ten characteristic parameters obtained from the reconstructed signals, as ANN input vector, and the PPG corresponding to the systolic blood pressure (SBP) and diastolic blood pressure (DBP), as the ANN vector output, were used to train the ANN model. The averaging error between the systolic pressure estimated by the proposed method and actual pressure value is 3.98 mmHg, and the standard deviation is 3.32 mmHg. The averaging error between the diastolic pressure estimated by the proposed method and actual pressure value is 3.81 mmHg, and the standard deviation is 3.63 mmHg. These results satisfy the American National Standards of the Association for the Advancement of Medical Instrumentation.

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Titel: Noninvasive Blood Pressure Measurement Based on Photoplethysmography
verfasst von: Yudong Wu
Shuncong Zhong
Verlag: Springer Singapore
Buch: Advanced Mechanical Science and Technology for the Industrial Revolution 4.0
Print ISBN: 978-981-10-4108-2

Electronic ISBN: 978-981-10-4109-9

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-981-10-4109-9_13

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