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Non invasive blood glucose estimation using green light photoplethysmography and machine learning.

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Title: Non invasive blood glucose estimation using green light photoplethysmography and machine learning.
Authors: Khan K; Department of Biomedical Engineering, Riphah International University, Islamabad, Pakistan., Malik L; Department of Biomedical Engineering, Riphah International University, Islamabad, Pakistan., Khan AQ; Department of Biomedical Engineering, Riphah International University, Islamabad, Pakistan., Abbasi SF; Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Birmingham, United Kingdom., Arvanitis TN; Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Birmingham, United Kingdom.
Source: Frontiers in digital health [Front Digit Health] 2026 Feb 17; Vol. 8, pp. 1705086. Date of Electronic Publication: 2026 Feb 17 (Print Publication: 2026).
Publication Type: Journal Article
Journal Info: Publisher: Frontiers Media S.A Country of Publication: Switzerland NLM ID: 101771889 Publication Model: eCollection Cited Medium: Internet ISSN: 2673-253X (Electronic) Linking ISSN: 2673253X NLM ISO Abbreviation: Front Digit Health Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
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PubType: Academic Journal
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  Data: Non invasive blood glucose estimation using green light photoplethysmography and machine learning.
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  Data: <searchLink fieldCode="AU" term="%22Khan+K%22">Khan K</searchLink>; Department of Biomedical Engineering, Riphah International University, Islamabad, Pakistan.<br /><searchLink fieldCode="AU" term="%22Malik+L%22">Malik L</searchLink>; Department of Biomedical Engineering, Riphah International University, Islamabad, Pakistan.<br /><searchLink fieldCode="AU" term="%22Khan+AQ%22">Khan AQ</searchLink>; Department of Biomedical Engineering, Riphah International University, Islamabad, Pakistan.<br /><searchLink fieldCode="AU" term="%22Abbasi+SF%22">Abbasi SF</searchLink>; Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Birmingham, United Kingdom.<br /><searchLink fieldCode="AU" term="%22Arvanitis+TN%22">Arvanitis TN</searchLink>; Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Birmingham, United Kingdom.
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  Data: <searchLink fieldCode="JN" term="%22101771889%22">Frontiers in digital health</searchLink> [Front Digit Health] 2026 Feb 17; Vol. 8, pp. 1705086. <i>Date of Electronic Publication: </i>2026 Feb 17 (<i>Print Publication: </i>2026).
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Frontiers+Media+S%2EA%22">Frontiers Media S.A </searchLink><i>Country of Publication: </i>Switzerland <i>NLM ID: </i>101771889 <i>Publication Model: </i>eCollection <i>Cited Medium: </i>Internet <i>ISSN: </i>2673-253X (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%222673253X%22">2673253X </searchLink><i>NLM ISO Abbreviation: </i>Front Digit Health <i>Subsets: </i>PubMed not MEDLINE
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        Value: 10.3389/fdgth.2026.1705086
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      – Code: eng
        Text: English
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        StartPage: 1705086
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      – TitleFull: Non invasive blood glucose estimation using green light photoplethysmography and machine learning.
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              Text: 2026 Feb 17
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