Blood pressure continuous measurement method based on PPG and multi-order differential signals thereof
The invention provides a blood pressure continuous measurement method based on PPG and multi-order differential signals thereof, and belongs to the technical field of blood pressure measurement methods. The method comprises the steps of S1, data analysis and acquisition, S2, signal denoising, S3, pe...
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creator | ZENG YU YU YING SHA JINLIANG WAN YIQIAN ZHANG YIFENG HONG HAN SUN QINGSHUO YE QING |
description | The invention provides a blood pressure continuous measurement method based on PPG and multi-order differential signals thereof, and belongs to the technical field of blood pressure measurement methods. The method comprises the steps of S1, data analysis and acquisition, S2, signal denoising, S3, period division, S4, quality evaluation screening and S5, blood pressure prediction model construction and evaluation. According to the method, multi-dimensional analysis is conducted on a PPG original signal, a first-order differential signal, a second-order differential signal and a third-order differential signal through a single-channel PPG signal, efficient filtering and quality evaluation screening, a BiLSTM network architecture in deep learning is adopted, a continuous blood pressure waveform prediction model is set up, and by means of a training mode of permutation and combination of different signal inputs, the blood pressure waveform prediction accuracy is improved. The optimal solution of the prediction er |
format | Patent |
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The method comprises the steps of S1, data analysis and acquisition, S2, signal denoising, S3, period division, S4, quality evaluation screening and S5, blood pressure prediction model construction and evaluation. According to the method, multi-dimensional analysis is conducted on a PPG original signal, a first-order differential signal, a second-order differential signal and a third-order differential signal through a single-channel PPG signal, efficient filtering and quality evaluation screening, a BiLSTM network architecture in deep learning is adopted, a continuous blood pressure waveform prediction model is set up, and by means of a training mode of permutation and combination of different signal inputs, the blood pressure waveform prediction accuracy is improved. 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The method comprises the steps of S1, data analysis and acquisition, S2, signal denoising, S3, period division, S4, quality evaluation screening and S5, blood pressure prediction model construction and evaluation. According to the method, multi-dimensional analysis is conducted on a PPG original signal, a first-order differential signal, a second-order differential signal and a third-order differential signal through a single-channel PPG signal, efficient filtering and quality evaluation screening, a BiLSTM network architecture in deep learning is adopted, a continuous blood pressure waveform prediction model is set up, and by means of a training mode of permutation and combination of different signal inputs, the blood pressure waveform prediction accuracy is improved. The optimal solution of the prediction er</description><subject>DIAGNOSIS</subject><subject>HUMAN NECESSITIES</subject><subject>HYGIENE</subject><subject>IDENTIFICATION</subject><subject>MEDICAL OR VETERINARY SCIENCE</subject><subject>SURGERY</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2023</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNijsOwjAQBd1QIOAOywFSRKAIlxDxqVAK-sjE68SSsxt57ftjJA5A9eaNZq3cJTBbWCKK5IgwMCVPmbPAjOarZqRUOE0lextBC0zQdXcwZGHOIfmKo8UI1juHsdTeBBA_kgkCaSqK3VatXLm4--1G7W_XV_uocOEeZTEDEqa-fdZ1c2r0Uevz4Z_mA6zsQBM</recordid><startdate>20231013</startdate><enddate>20231013</enddate><creator>ZENG YU</creator><creator>YU YING</creator><creator>SHA JINLIANG</creator><creator>WAN YIQIAN</creator><creator>ZHANG YIFENG</creator><creator>HONG HAN</creator><creator>SUN QINGSHUO</creator><creator>YE QING</creator><scope>EVB</scope></search><sort><creationdate>20231013</creationdate><title>Blood pressure continuous measurement method based on PPG and multi-order differential signals thereof</title><author>ZENG YU ; YU YING ; SHA JINLIANG ; WAN YIQIAN ; ZHANG YIFENG ; HONG HAN ; SUN QINGSHUO ; YE QING</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN116869499A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2023</creationdate><topic>DIAGNOSIS</topic><topic>HUMAN NECESSITIES</topic><topic>HYGIENE</topic><topic>IDENTIFICATION</topic><topic>MEDICAL OR VETERINARY SCIENCE</topic><topic>SURGERY</topic><toplevel>online_resources</toplevel><creatorcontrib>ZENG YU</creatorcontrib><creatorcontrib>YU YING</creatorcontrib><creatorcontrib>SHA JINLIANG</creatorcontrib><creatorcontrib>WAN YIQIAN</creatorcontrib><creatorcontrib>ZHANG YIFENG</creatorcontrib><creatorcontrib>HONG HAN</creatorcontrib><creatorcontrib>SUN QINGSHUO</creatorcontrib><creatorcontrib>YE QING</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>ZENG YU</au><au>YU YING</au><au>SHA JINLIANG</au><au>WAN YIQIAN</au><au>ZHANG YIFENG</au><au>HONG HAN</au><au>SUN QINGSHUO</au><au>YE QING</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Blood pressure continuous measurement method based on PPG and multi-order differential signals thereof</title><date>2023-10-13</date><risdate>2023</risdate><abstract>The invention provides a blood pressure continuous measurement method based on PPG and multi-order differential signals thereof, and belongs to the technical field of blood pressure measurement methods. The method comprises the steps of S1, data analysis and acquisition, S2, signal denoising, S3, period division, S4, quality evaluation screening and S5, blood pressure prediction model construction and evaluation. According to the method, multi-dimensional analysis is conducted on a PPG original signal, a first-order differential signal, a second-order differential signal and a third-order differential signal through a single-channel PPG signal, efficient filtering and quality evaluation screening, a BiLSTM network architecture in deep learning is adopted, a continuous blood pressure waveform prediction model is set up, and by means of a training mode of permutation and combination of different signal inputs, the blood pressure waveform prediction accuracy is improved. The optimal solution of the prediction er</abstract><oa>free_for_read</oa></addata></record> |
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title | Blood pressure continuous measurement method based on PPG and multi-order differential signals thereof |
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