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| Research About Cuff-less Continuous Blood Pressure Estimation by Multi-parameter Fusion Method |
| XU Zhihong①② FANG Zhen①② CHEN Xianxiang① QIN Li②③ DU Lidong① ZHAO Zhan①② LIU Jiexin④ |
①(Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China)
②(University of Chinese Academy of Sciences, Beijing 100049, China)
③(Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China)
④(TianTan Hospital, Beijing 100050, China) |
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Abstract For the problem of noninvasive continuous blood pressure algorithm with un-accuracy, a novel multi- parameter fusion algorithm based on BP neural network is proposed, according to the formation from electrocardiogram and photoplethysmograph of arterial blood pressure. The improved Pan Tompkins algorithm is used to extract the R peak of electrocardiogram, and difference-threshold algorithm is used to extract the features points of photo-plethysmograph, and the fifteen feature parameters relative to blood pressure are extracted and used to establish the model of blood pressure to estimate the beat-to-beat systolic blood pressure and diastolic blood pressure. The factor analysis method is used to analyze the weight of each parameter. The results show that the weight order is pulse transit time, time information, photoplethysmography area information, amplitude information and area ratio. The algorithm is tested in the TianTan Hospital, and the means±standard difference of single measurement errors are respectively -1.57±6.12 mmHg and -0.62±4.82 mmHg, the means± standard difference, D. of repeated measurement errors are respectively -2.12±5.10 mmHg and -2.52±4.41 mmHg, for systolic blood pressure and diastolic blood pressure. And the measurement accuracy for systolic blood pressure and diastolic blood pressure reaches Grade A of BHS standard and AAMI standard.
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Received: 24 March 2017
Published: 03 January 2018
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| Fund:The National Natural Science Foundation of China (61302033), The Key Project of Beijing Municipal Natural Science Foundation (Z16003), The National Key Research and Development Project (2016YFC1304302) |
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Corresponding Authors:
FANG Zhen
E-mail: zfang@mail.ie.ac.cn
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