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Cuff-less PPG based continuous blood pressure monitoring - A smartphone based approach
Cuff-less estimation of systolic (SBP) and diastolic (DBP) blood pressure is an efficient approach for non-invasive and continuous monitoring of an individual's vitals. Although pulse transit time (PTT) based approaches have been successful in estimating the systolic and diastolic blood pressur...
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creator | Gaurav, Aman Maheedhar, Maram Tiwari, Vijay N. Narayanan, Rangavittal |
description | Cuff-less estimation of systolic (SBP) and diastolic (DBP) blood pressure is an efficient approach for non-invasive and continuous monitoring of an individual's vitals. Although pulse transit time (PTT) based approaches have been successful in estimating the systolic and diastolic blood pressures to a reasonable degree of accuracy, there is still scope for improvement in terms of accuracies. Moreover, PTT approach requires data from sensors placed at two different locations along with individual calibration of physiological parameters for deriving correct estimation of systolic and diastolic blood pressure (BP) and hence is not suitable for smartphone deployment. Heart Rate Variability is one of the extensively used non-invasive parameters to assess cardiovascular autonomic nervous system and is known to be associated with SBP and DBP indirectly. In this work, we propose a novel method to extract a comprehensive set of features by combining PPG signal based and Heart Rate Variability (HRV) related features using a single PPG sensor. Further, these features are fed into a DBP feedback based combinatorial neural network model to arrive at a common weighted average output of DBP and subsequently SBP. Our results show that using this current approach, an accuracy of ±6.8 mmHg for SBP and ±4.7 mmHg for DBP is achievable on 1,750,000 pulses extracted from a public database (comprising 3000 people). Since most of the smartphones are now equipped with PPG sensor, a mobile based cuff-less BP estimation will enable the user to monitor their BP as a vital parameter on demand. This will open new avenues towards development of pervasive and continuous BP monitoring systems leading to an early detection and prevention of cardiovascular diseases. |
doi_str_mv | 10.1109/EMBC.2016.7590775 |
format | conference_proceeding |
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Although pulse transit time (PTT) based approaches have been successful in estimating the systolic and diastolic blood pressures to a reasonable degree of accuracy, there is still scope for improvement in terms of accuracies. Moreover, PTT approach requires data from sensors placed at two different locations along with individual calibration of physiological parameters for deriving correct estimation of systolic and diastolic blood pressure (BP) and hence is not suitable for smartphone deployment. Heart Rate Variability is one of the extensively used non-invasive parameters to assess cardiovascular autonomic nervous system and is known to be associated with SBP and DBP indirectly. In this work, we propose a novel method to extract a comprehensive set of features by combining PPG signal based and Heart Rate Variability (HRV) related features using a single PPG sensor. Further, these features are fed into a DBP feedback based combinatorial neural network model to arrive at a common weighted average output of DBP and subsequently SBP. Our results show that using this current approach, an accuracy of ±6.8 mmHg for SBP and ±4.7 mmHg for DBP is achievable on 1,750,000 pulses extracted from a public database (comprising 3000 people). Since most of the smartphones are now equipped with PPG sensor, a mobile based cuff-less BP estimation will enable the user to monitor their BP as a vital parameter on demand. This will open new avenues towards development of pervasive and continuous BP monitoring systems leading to an early detection and prevention of cardiovascular diseases.</description><identifier>ISSN: 1557-170X</identifier><identifier>EISSN: 2694-0604</identifier><identifier>EISBN: 1457702207</identifier><identifier>EISBN: 9781457702204</identifier><identifier>DOI: 10.1109/EMBC.2016.7590775</identifier><identifier>PMID: 28268403</identifier><language>eng</language><publisher>IEEE</publisher><subject>Biomedical monitoring ; Blood pressure ; Estimation ; Feature extraction ; Heart rate variability ; Monitoring ; Sensors</subject><ispartof>2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016, Vol.2016, p.607-610</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c279t-8fd13313e432ab541e2446e83dae85f3cb9d0a2b0290f387d727d5242f0210943</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>309,310,314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Gaurav, Aman</creatorcontrib><creatorcontrib>Maheedhar, Maram</creatorcontrib><creatorcontrib>Tiwari, Vijay N.</creatorcontrib><creatorcontrib>Narayanan, Rangavittal</creatorcontrib><title>Cuff-less PPG based continuous blood pressure monitoring - A smartphone based approach</title><title>2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)</title><addtitle>EMBC</addtitle><description>Cuff-less estimation of systolic (SBP) and diastolic (DBP) blood pressure is an efficient approach for non-invasive and continuous monitoring of an individual's vitals. Although pulse transit time (PTT) based approaches have been successful in estimating the systolic and diastolic blood pressures to a reasonable degree of accuracy, there is still scope for improvement in terms of accuracies. Moreover, PTT approach requires data from sensors placed at two different locations along with individual calibration of physiological parameters for deriving correct estimation of systolic and diastolic blood pressure (BP) and hence is not suitable for smartphone deployment. Heart Rate Variability is one of the extensively used non-invasive parameters to assess cardiovascular autonomic nervous system and is known to be associated with SBP and DBP indirectly. In this work, we propose a novel method to extract a comprehensive set of features by combining PPG signal based and Heart Rate Variability (HRV) related features using a single PPG sensor. Further, these features are fed into a DBP feedback based combinatorial neural network model to arrive at a common weighted average output of DBP and subsequently SBP. Our results show that using this current approach, an accuracy of ±6.8 mmHg for SBP and ±4.7 mmHg for DBP is achievable on 1,750,000 pulses extracted from a public database (comprising 3000 people). Since most of the smartphones are now equipped with PPG sensor, a mobile based cuff-less BP estimation will enable the user to monitor their BP as a vital parameter on demand. This will open new avenues towards development of pervasive and continuous BP monitoring systems leading to an early detection and prevention of cardiovascular diseases.</description><subject>Biomedical monitoring</subject><subject>Blood pressure</subject><subject>Estimation</subject><subject>Feature extraction</subject><subject>Heart rate variability</subject><subject>Monitoring</subject><subject>Sensors</subject><issn>1557-170X</issn><issn>2694-0604</issn><isbn>1457702207</isbn><isbn>9781457702204</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2016</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotkL1Ow0AQhA8EIibkARDNlTQOe3u_LoMVAlIQKQDRWWf7TIwcn_HZBW-PpaSa5pvZ2SHklsGSMUge1q-P6RKBqaWWCWgtz8g1E1JrQAR9TiJUiYhBgbggEZNSx0zD14wsQvgBAKaV4iivyAwNKiOAR-QzHasqblwIdLfb0NwGV9LCt0Pdjn4MNG-8L2nXT8DYO3rwbT34vm6_aUxXNBxsP3R737qT03Zd722xvyGXlW2CW5x0Tj6e1u_pc7x927ykq21coE6G2FQl45xxJzjaXArmUAjlDC-tM7LiRZ6UYDEHTKDiRpcadSlRYAU4DSL4nNwfc6ezv6MLQ3aoQ-GaxrZuqp8xo-X0p1JmQu-OaO2cy7q-nrr_Zach-T8YKWHh</recordid><startdate>201608</startdate><enddate>201608</enddate><creator>Gaurav, Aman</creator><creator>Maheedhar, Maram</creator><creator>Tiwari, Vijay N.</creator><creator>Narayanan, Rangavittal</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope><scope>7X8</scope></search><sort><creationdate>201608</creationdate><title>Cuff-less PPG based continuous blood pressure monitoring - A smartphone based approach</title><author>Gaurav, Aman ; Maheedhar, Maram ; Tiwari, Vijay N. ; Narayanan, Rangavittal</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c279t-8fd13313e432ab541e2446e83dae85f3cb9d0a2b0290f387d727d5242f0210943</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Biomedical monitoring</topic><topic>Blood pressure</topic><topic>Estimation</topic><topic>Feature extraction</topic><topic>Heart rate variability</topic><topic>Monitoring</topic><topic>Sensors</topic><toplevel>online_resources</toplevel><creatorcontrib>Gaurav, Aman</creatorcontrib><creatorcontrib>Maheedhar, Maram</creatorcontrib><creatorcontrib>Tiwari, Vijay N.</creatorcontrib><creatorcontrib>Narayanan, Rangavittal</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection><collection>MEDLINE - Academic</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gaurav, Aman</au><au>Maheedhar, Maram</au><au>Tiwari, Vijay N.</au><au>Narayanan, Rangavittal</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Cuff-less PPG based continuous blood pressure monitoring - A smartphone based approach</atitle><btitle>2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)</btitle><stitle>EMBC</stitle><date>2016-08</date><risdate>2016</risdate><volume>2016</volume><spage>607</spage><epage>610</epage><pages>607-610</pages><issn>1557-170X</issn><eissn>2694-0604</eissn><eisbn>1457702207</eisbn><eisbn>9781457702204</eisbn><abstract>Cuff-less estimation of systolic (SBP) and diastolic (DBP) blood pressure is an efficient approach for non-invasive and continuous monitoring of an individual's vitals. Although pulse transit time (PTT) based approaches have been successful in estimating the systolic and diastolic blood pressures to a reasonable degree of accuracy, there is still scope for improvement in terms of accuracies. Moreover, PTT approach requires data from sensors placed at two different locations along with individual calibration of physiological parameters for deriving correct estimation of systolic and diastolic blood pressure (BP) and hence is not suitable for smartphone deployment. Heart Rate Variability is one of the extensively used non-invasive parameters to assess cardiovascular autonomic nervous system and is known to be associated with SBP and DBP indirectly. In this work, we propose a novel method to extract a comprehensive set of features by combining PPG signal based and Heart Rate Variability (HRV) related features using a single PPG sensor. Further, these features are fed into a DBP feedback based combinatorial neural network model to arrive at a common weighted average output of DBP and subsequently SBP. Our results show that using this current approach, an accuracy of ±6.8 mmHg for SBP and ±4.7 mmHg for DBP is achievable on 1,750,000 pulses extracted from a public database (comprising 3000 people). Since most of the smartphones are now equipped with PPG sensor, a mobile based cuff-less BP estimation will enable the user to monitor their BP as a vital parameter on demand. This will open new avenues towards development of pervasive and continuous BP monitoring systems leading to an early detection and prevention of cardiovascular diseases.</abstract><pub>IEEE</pub><pmid>28268403</pmid><doi>10.1109/EMBC.2016.7590775</doi><tpages>4</tpages></addata></record> |
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language | eng |
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source | IEEE Xplore All Conference Series |
subjects | Biomedical monitoring Blood pressure Estimation Feature extraction Heart rate variability Monitoring Sensors |
title | Cuff-less PPG based continuous blood pressure monitoring - A smartphone based approach |
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