Real-time human blood pressure measurement based on laser self-mixing interferometry with extreme learning machine
In this paper, we present a method based on self-mixing interferometry combing extreme learning machine for real-time human blood pressure measurement. A signal processing method based on wavelet transform is applied to extract reversion point in the self-mixing interference signal, thus the pulse w...
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| Veröffentlicht in: | Optoelectronics letters 2020-11, Vol.16 (6), p.467-470 |
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| creator | Wang, Xiu-lin Lü, Li-ping Hu, Lu Huang, Wen-cai |
| description | In this paper, we present a method based on self-mixing interferometry combing extreme learning machine for real-time human blood pressure measurement. A signal processing method based on wavelet transform is applied to extract reversion point in the self-mixing interference signal, thus the pulse wave profile is successfully reconstructed. Considering the blood pressure values are intrinsically related to characteristic parameters of the pulse wave, 80 samples from the MIMIC-II database are used to train the extreme learning machine blood pressure model. In the experiment, 15 measured samples of pulse wave signal are used as the prediction sets. The results show that the errors of systolic and diastolic blood pressure are both within 5 mmHg compared with that by the Coriolis method. |
| doi_str_mv | 10.1007/s11801-020-0050-x |
| format | Article |
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A signal processing method based on wavelet transform is applied to extract reversion point in the self-mixing interference signal, thus the pulse wave profile is successfully reconstructed. Considering the blood pressure values are intrinsically related to characteristic parameters of the pulse wave, 80 samples from the MIMIC-II database are used to train the extreme learning machine blood pressure model. In the experiment, 15 measured samples of pulse wave signal are used as the prediction sets. The results show that the errors of systolic and diastolic blood pressure are both within 5 mmHg compared with that by the Coriolis method.</description><identifier>ISSN: 1673-1905</identifier><identifier>EISSN: 1993-5013</identifier><identifier>DOI: 10.1007/s11801-020-0050-x</identifier><language>eng</language><publisher>Tianjin: Tianjin University of Technology</publisher><subject>Artificial neural networks ; Blood pressure ; Coriolis force ; Interferometry ; Lasers ; Machine learning ; Optical Devices ; Optics ; Photonics ; Physics ; Physics and Astronomy ; Pressure measurement ; Real time ; Signal processing ; Wavelet transforms</subject><ispartof>Optoelectronics letters, 2020-11, Vol.16 (6), p.467-470</ispartof><rights>Tianjin University of Technology and Springer-Verlag GmbH Germany, part of Springer Nature 2020</rights><rights>Tianjin University of Technology and Springer-Verlag GmbH Germany, part of Springer Nature 2020.</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c316t-a123dd42306131e2291ab9280a28ffb11db6338a648b3cee174ea05ece999bb53</citedby><cites>FETCH-LOGICAL-c316t-a123dd42306131e2291ab9280a28ffb11db6338a648b3cee174ea05ece999bb53</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11801-020-0050-x$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11801-020-0050-x$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Wang, Xiu-lin</creatorcontrib><creatorcontrib>Lü, Li-ping</creatorcontrib><creatorcontrib>Hu, Lu</creatorcontrib><creatorcontrib>Huang, Wen-cai</creatorcontrib><title>Real-time human blood pressure measurement based on laser self-mixing interferometry with extreme learning machine</title><title>Optoelectronics letters</title><addtitle>Optoelectron. Lett</addtitle><description>In this paper, we present a method based on self-mixing interferometry combing extreme learning machine for real-time human blood pressure measurement. A signal processing method based on wavelet transform is applied to extract reversion point in the self-mixing interference signal, thus the pulse wave profile is successfully reconstructed. Considering the blood pressure values are intrinsically related to characteristic parameters of the pulse wave, 80 samples from the MIMIC-II database are used to train the extreme learning machine blood pressure model. In the experiment, 15 measured samples of pulse wave signal are used as the prediction sets. The results show that the errors of systolic and diastolic blood pressure are both within 5 mmHg compared with that by the Coriolis method.</description><subject>Artificial neural networks</subject><subject>Blood pressure</subject><subject>Coriolis force</subject><subject>Interferometry</subject><subject>Lasers</subject><subject>Machine learning</subject><subject>Optical Devices</subject><subject>Optics</subject><subject>Photonics</subject><subject>Physics</subject><subject>Physics and Astronomy</subject><subject>Pressure measurement</subject><subject>Real time</subject><subject>Signal processing</subject><subject>Wavelet transforms</subject><issn>1673-1905</issn><issn>1993-5013</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp1kEtLxDAUhYsoOIzzA9wFXEfvTfrKUgZfIAii65C0tzMd2nRMWuz8e1squPJuzll851w4UXSNcIsA2V1AzAE5COAACfDxLFqhUpIngPJ88mkmOSpILqNNCAeYToosj9Uq8u9kGt7XLbH90BrHbNN1JTt6CmHwxFoys7bkemZNoJJ1jjWT8SxQU_G2Hmu3Y7XryVfku5Z6f2Lfdb9nNPZzkDVkvJuh1hT72tFVdFGZJtDmV9fR5-PDx_aZv749vWzvX3khMe25QSHLMhYSUpRIQig0VokcjMiryiKWNpUyN2mcW1kQYRaTgYQKUkpZm8h1dLP0Hn33NVDo9aEbvJteahFnMgelcjFRuFCF70LwVOmjr1vjTxpBz-vqZV09ravndfU4ZcSSCRPrduT_mv8P_QDaBX8O</recordid><startdate>20201101</startdate><enddate>20201101</enddate><creator>Wang, Xiu-lin</creator><creator>Lü, Li-ping</creator><creator>Hu, Lu</creator><creator>Huang, Wen-cai</creator><general>Tianjin University of Technology</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20201101</creationdate><title>Real-time human blood pressure measurement based on laser self-mixing interferometry with extreme learning machine</title><author>Wang, Xiu-lin ; Lü, Li-ping ; Hu, Lu ; Huang, Wen-cai</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c316t-a123dd42306131e2291ab9280a28ffb11db6338a648b3cee174ea05ece999bb53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Artificial neural networks</topic><topic>Blood pressure</topic><topic>Coriolis force</topic><topic>Interferometry</topic><topic>Lasers</topic><topic>Machine learning</topic><topic>Optical Devices</topic><topic>Optics</topic><topic>Photonics</topic><topic>Physics</topic><topic>Physics and Astronomy</topic><topic>Pressure measurement</topic><topic>Real time</topic><topic>Signal processing</topic><topic>Wavelet transforms</topic><toplevel>online_resources</toplevel><creatorcontrib>Wang, Xiu-lin</creatorcontrib><creatorcontrib>Lü, Li-ping</creatorcontrib><creatorcontrib>Hu, Lu</creatorcontrib><creatorcontrib>Huang, Wen-cai</creatorcontrib><collection>CrossRef</collection><jtitle>Optoelectronics letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Xiu-lin</au><au>Lü, Li-ping</au><au>Hu, Lu</au><au>Huang, Wen-cai</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Real-time human blood pressure measurement based on laser self-mixing interferometry with extreme learning machine</atitle><jtitle>Optoelectronics letters</jtitle><stitle>Optoelectron. Lett</stitle><date>2020-11-01</date><risdate>2020</risdate><volume>16</volume><issue>6</issue><spage>467</spage><epage>470</epage><pages>467-470</pages><issn>1673-1905</issn><eissn>1993-5013</eissn><abstract>In this paper, we present a method based on self-mixing interferometry combing extreme learning machine for real-time human blood pressure measurement. A signal processing method based on wavelet transform is applied to extract reversion point in the self-mixing interference signal, thus the pulse wave profile is successfully reconstructed. Considering the blood pressure values are intrinsically related to characteristic parameters of the pulse wave, 80 samples from the MIMIC-II database are used to train the extreme learning machine blood pressure model. In the experiment, 15 measured samples of pulse wave signal are used as the prediction sets. The results show that the errors of systolic and diastolic blood pressure are both within 5 mmHg compared with that by the Coriolis method.</abstract><cop>Tianjin</cop><pub>Tianjin University of Technology</pub><doi>10.1007/s11801-020-0050-x</doi><tpages>4</tpages></addata></record> |
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| source | SpringerNature Complete Journals; Alma/SFX Local Collection |
| subjects | Artificial neural networks Blood pressure Coriolis force Interferometry Lasers Machine learning Optical Devices Optics Photonics Physics Physics and Astronomy Pressure measurement Real time Signal processing Wavelet transforms |
| title | Real-time human blood pressure measurement based on laser self-mixing interferometry with extreme learning machine |
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