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

Paul C.-P. Chao, Tse Yi Tu

研究成果: Conference contribution同行評審

3 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題ASME 2017 Conference on Information Storage and Processing Systems, ISPS 2017
發行者American Society of Mechanical Engineers
ISBN(電子)9780791858103
DOIs
出版狀態Published - 1 一月 2017
事件ASME 2017 Conference on Information Storage and Processing Systems, ISPS 2017 - San Francisco, United States
持續時間: 29 八月 201730 八月 2017

出版系列

名字ASME 2017 Conference on Information Storage and Processing Systems, ISPS 2017

Conference

ConferenceASME 2017 Conference on Information Storage and Processing Systems, ISPS 2017
國家United States
城市San Francisco
期間29/08/1730/08/17

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