Enhancing the Robustness of Smartphone Photoplethysmography: A Signal Quality Index Approach
Identifieur interne : 000515 ( Main/Exploration ); précédent : 000514; suivant : 000516Enhancing the Robustness of Smartphone Photoplethysmography: A Signal Quality Index Approach
Auteurs : Ivan Liu ; Shiguang Ni [République populaire de Chine] ; Kaiping Peng [République populaire de Chine]Source :
- Sensors (Basel, Switzerland) [ 1424-8220 ] ; 2020.
Abstract
Heart rate variability (HRV) provides essential health information such as the risks of heart attacks and mental disorders. However, inconvenience related to the accurate detection of HRV limits its potential applications. The ubiquitous use of smartphones makes them an excellent choice for regular and portable health monitoring. Following this trend, smartphone photoplethysmography (PPG) has recently garnered prominence; however, the lack of robustness has prevented both researchers and practitioners from embracing this technology. This study aimed to bridge the gap in the literature by developing a novel smartphone PPG quality index (SPQI) that can filter corrupted data. A total of 226 participants joined the study, and results from 1343 samples were used to validate the proposed sinusoidal function-based model. In both the correlation coefficient and Bland–Altman analyses, the agreement between HRV measurements generated by both the smartphone PPG and the reference electrocardiogram improved when data were filtered through the SPQI. Our results support not only the proposed approach but also the general value of using smartphone PPG in HRV analysis.
Url:
DOI: 10.3390/s20071923
PubMed: 32235543
PubMed Central: 7181214
Affiliations:
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type="wicri:Area/Main/Curation">000515</idno><idno type="wicri:Area/Main/Exploration">000515</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Enhancing the Robustness of Smartphone Photoplethysmography: A Signal Quality Index Approach</title><author><name sortKey="Liu, Ivan" sort="Liu, Ivan" uniqKey="Liu I" first="Ivan" last="Liu">Ivan Liu</name><affiliation><nlm:aff id="af1-sensors-20-01923">Data Science and Information Technology Research Center, Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen 518055, China;<email>liusq15@mails.tsinghua.edu.cn</email> (I.L.);<email>pengkp@tsinghua.edu.cn</email> (K.P.)</nlm:aff></affiliation></author><author><name sortKey="Ni, Shiguang" sort="Ni, Shiguang" uniqKey="Ni S" first="Shiguang" last="Ni">Shiguang Ni</name><affiliation wicri:level="1"><nlm:aff id="af2-sensors-20-01923">Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China</nlm:aff><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055</wicri:regionArea><placeName><settlement type="city">Shenzhen</settlement><region type="province">Guangdong</region></placeName></affiliation></author><author><name sortKey="Peng, Kaiping" sort="Peng, Kaiping" uniqKey="Peng K" first="Kaiping" last="Peng">Kaiping Peng</name><affiliation><nlm:aff id="af1-sensors-20-01923">Data Science and Information Technology Research Center, Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen 518055, China;<email>liusq15@mails.tsinghua.edu.cn</email> (I.L.);<email>pengkp@tsinghua.edu.cn</email> (K.P.)</nlm:aff></affiliation><affiliation wicri:level="1"><nlm:aff id="af3-sensors-20-01923">Department of Psychology, Tsinghua University, Beijing 100084, China</nlm:aff><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Psychology, Tsinghua University, Beijing 100084</wicri:regionArea><placeName><settlement type="city">Pékin</settlement></placeName></affiliation></author></analytic><series><title level="j">Sensors (Basel, Switzerland)</title><idno type="eISSN">1424-8220</idno><imprint><date when="2020">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Heart rate variability (HRV) provides essential health information such as the risks of heart attacks and mental disorders. However, inconvenience related to the accurate detection of HRV limits its potential applications. The ubiquitous use of smartphones makes them an excellent choice for regular and portable health monitoring. Following this trend, smartphone photoplethysmography (PPG) has recently garnered prominence; however, the lack of robustness has prevented both researchers and practitioners from embracing this technology. This study aimed to bridge the gap in the literature by developing a novel smartphone PPG quality index (SPQI) that can filter corrupted data. A total of 226 participants joined the study, and results from 1343 samples were used to validate the proposed sinusoidal function-based model. In both the correlation coefficient and Bland–Altman analyses, the agreement between HRV measurements generated by both the smartphone PPG and the reference electrocardiogram improved when data were filtered through the SPQI. 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Ho</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Lomaliza, J P" uniqKey="Lomaliza J">J.P. Lomaliza</name></author><author><name sortKey="Park, H" uniqKey="Park H">H. Park</name></author></analytic></biblStruct></listBibl></div1></back></TEI><affiliations><list><country><li>République populaire de Chine</li></country><region><li>Guangdong</li></region><settlement><li>Pékin</li><li>Shenzhen</li></settlement></list><tree><noCountry><name sortKey="Liu, Ivan" sort="Liu, Ivan" uniqKey="Liu I" first="Ivan" last="Liu">Ivan Liu</name></noCountry><country name="République populaire de Chine"><region name="Guangdong"><name sortKey="Ni, Shiguang" sort="Ni, Shiguang" uniqKey="Ni S" first="Shiguang" last="Ni">Shiguang Ni</name></region><name sortKey="Peng, Kaiping" sort="Peng, Kaiping" uniqKey="Peng K" first="Kaiping" last="Peng">Kaiping Peng</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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