Using the time-domain characterization for estimation continuous blood pressure via neural network method

Paul C.-P. Chao, Tse Yi Tu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

The new method with back-propagation neural network is expected to be capable of continuous measurement of blood pressures with noninvasive, cuffless strain blood pressure sensor. The eight time-domain characterizations estimate systolic blood pressure and diastolic blood pressure via BPNN leading to a satisfactory accuracy of the BP sensor. The BP sensor is used on human wrist to collect the continuously pulse signal for measuring blood pressures. To assist the sensor, a readout circuit is devised with a Wheatstone bridge, amplifier, filter, and a digital signal processor. The results of SBP and DBP are 4.27±4.98 mmHg and 3.86±5.35 mmHg, respectively. The errors of blood pressure pass the criteria for Association for the Advancement of Medical Instrumentation (AAMI) method 2 and the British Hypertension Society (BHS) Grade B.

Original languageEnglish
Title of host publicationASME 2017 Conference on Information Storage and Processing Systems, ISPS 2017
PublisherAmerican Society of Mechanical Engineers
ISBN (Electronic)9780791858103
DOIs
StatePublished - 1 Jan 2017
EventASME 2017 Conference on Information Storage and Processing Systems, ISPS 2017 - San Francisco, United States
Duration: 29 Aug 201730 Aug 2017

Publication series

NameASME 2017 Conference on Information Storage and Processing Systems, ISPS 2017

Conference

ConferenceASME 2017 Conference on Information Storage and Processing Systems, ISPS 2017
CountryUnited States
CitySan Francisco
Period29/08/1730/08/17

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