Age group classification based on optical measurement of brain pulsation using machine learning
Abstract Optical techniques, such as functional near-infrared spectroscopy (fNIRS), contain high potential for the development of non-invasive wearable systems for evaluating cerebral vascular condition in aging, due to their portability and ability to monitor real-time changes in cerebral hemodynam...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-87645-w |
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| author | Martti Ilvesmäki Hany Ferdinando Kai Noponen Tapio Seppänen Vesa Korhonen Vesa Kiviniemi Teemu Myllylä |
| author_facet | Martti Ilvesmäki Hany Ferdinando Kai Noponen Tapio Seppänen Vesa Korhonen Vesa Kiviniemi Teemu Myllylä |
| author_sort | Martti Ilvesmäki |
| collection | DOAJ |
| description | Abstract Optical techniques, such as functional near-infrared spectroscopy (fNIRS), contain high potential for the development of non-invasive wearable systems for evaluating cerebral vascular condition in aging, due to their portability and ability to monitor real-time changes in cerebral hemodynamics. In this study, thirty-six healthy adults were measured by single channel fNIRS to explore differences between two age groups using machine learning (ML). The subjects, measured during functional magnetic resonance imaging (fMRI) at Oulu University Hospital, were divided into young (age ≤ 32) and elderly (age ≥ 57) groups. Brain pulses were extracted from fNIRS using a single 830 nm wavelength. Four feature sets were derived from log-normal parameters estimated by pulse decomposition algorithm. ML experiments utilized support vector machines and random forest learners, along with maximum relevance minimum redundancy and principal component analysis for feature selection. Performance with increasing sample size was estimated using learning curve method. The best mean balanced accuracies for each feature set were over 75% (75.9%, 76.4%, 79.3%, 76.9%), indicating the pulse features containing age related information. Learning curves indicated stable classification performance with increasing sample size. The results demonstrate the potential of using single channel fNIRS in the analysis of aging. |
| format | Article |
| id | doaj-art-65f9cf810be045419b3c1629a17cb795 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-65f9cf810be045419b3c1629a17cb7952025-01-26T12:26:22ZengNature PortfolioScientific Reports2045-23222025-01-0115111510.1038/s41598-025-87645-wAge group classification based on optical measurement of brain pulsation using machine learningMartti Ilvesmäki0Hany Ferdinando1Kai Noponen2Tapio Seppänen3Vesa Korhonen4Vesa Kiviniemi5Teemu Myllylä6Research Unit of Health Sciences and Technology, University of OuluResearch Unit of Health Sciences and Technology, University of OuluCenter for Machine Vision and Signal Analysis Research Unit, University of OuluCenter for Machine Vision and Signal Analysis Research Unit, University of OuluResearch Unit of Health Sciences and Technology, University of OuluResearch Unit of Health Sciences and Technology, University of OuluResearch Unit of Health Sciences and Technology, University of OuluAbstract Optical techniques, such as functional near-infrared spectroscopy (fNIRS), contain high potential for the development of non-invasive wearable systems for evaluating cerebral vascular condition in aging, due to their portability and ability to monitor real-time changes in cerebral hemodynamics. In this study, thirty-six healthy adults were measured by single channel fNIRS to explore differences between two age groups using machine learning (ML). The subjects, measured during functional magnetic resonance imaging (fMRI) at Oulu University Hospital, were divided into young (age ≤ 32) and elderly (age ≥ 57) groups. Brain pulses were extracted from fNIRS using a single 830 nm wavelength. Four feature sets were derived from log-normal parameters estimated by pulse decomposition algorithm. ML experiments utilized support vector machines and random forest learners, along with maximum relevance minimum redundancy and principal component analysis for feature selection. Performance with increasing sample size was estimated using learning curve method. The best mean balanced accuracies for each feature set were over 75% (75.9%, 76.4%, 79.3%, 76.9%), indicating the pulse features containing age related information. Learning curves indicated stable classification performance with increasing sample size. The results demonstrate the potential of using single channel fNIRS in the analysis of aging.https://doi.org/10.1038/s41598-025-87645-w |
| spellingShingle | Martti Ilvesmäki Hany Ferdinando Kai Noponen Tapio Seppänen Vesa Korhonen Vesa Kiviniemi Teemu Myllylä Age group classification based on optical measurement of brain pulsation using machine learning Scientific Reports |
| title | Age group classification based on optical measurement of brain pulsation using machine learning |
| title_full | Age group classification based on optical measurement of brain pulsation using machine learning |
| title_fullStr | Age group classification based on optical measurement of brain pulsation using machine learning |
| title_full_unstemmed | Age group classification based on optical measurement of brain pulsation using machine learning |
| title_short | Age group classification based on optical measurement of brain pulsation using machine learning |
| title_sort | age group classification based on optical measurement of brain pulsation using machine learning |
| url | https://doi.org/10.1038/s41598-025-87645-w |
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