[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128213-en":3,"doc-seo-128213-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128213,2336475104362,"Eden","https://ap-avatar.wpscdn.com/avatar/22000c4c46a41b752dd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786595829695023868",8,"Research & Report","Age group classification based on optical measurement of brain pulsation using machine learning","Optical near-infrared measurements, particularly single-channel fNIRS, are evaluated as a non-invasive approach for extracting age-related cerebral pulsation information. Thirty-six healthy adults were scanned using fNIRS while undergoing fMRI, then separated into young (≤32) and elderly (≥57) groups. Brain pulses were derived from a single 830 nm wavelength and decomposed into log-normal feature sets. Support vector machines and random forests with feature selection via mRMR and PCA were tested, with learning-curve evaluation showing stable classification and best balanced accuracies above 75%.","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nAge group classification based on optical measurement of brain pulsation using machine learning  \nMartti Ilvesmäki1􀀍, Hany Ferdinando1, Kai Noponen2, Tapio Seppänen2, Vesa Korhonen1,3, Vesa Kiviniemi1,3 & Teemu Myllylä1,4  \nOptical 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.  \nThe life expectancy of the global population is steadily continuing to increase1. As aging possess an increased risk of developing age-related diseases, such as Alzheimer’s disease (AD), the evaluation of aging and brain health is essential. Brain aging has been widely studied, and multiple interconnected structural and functional changes have been reported2. Healthy brain aging is accompanied by global brain atrophy, and a link between stiffened cerebral arterial system and loss of brain’s grey and white matter volume has been reported3. Furthermore, the more compliant arteries of younger subjects transmit impulses more effectively along arterial walls, facilitating cerebrospinal fluid (CSF) solute convection within perivascular spaces. In contrast, stiffened arteries with degraded elastin and collagen exhibit reduced compliance, leading to microvascular damage and potential target organ damage4, altered permeability5, and reduced or even reversed CSF solute convection6. The reduction of elasticity of arteries is known as arterial stiffness, which is a leading marker of hypertension, and is commonly used an index for vascular aging4.  \nThe most commonly used method to assess arterial stiffness is the use of carotid-to-femoral pulse wave velocity (PWV) measurement7, where the propagation of pressure pulse is measured from carotid to femoral arteries. As the measurement distance is known, the PWV can be computed. The increased PWV indicates stiffened arteries or increased arterial wall muscle tone. In addition to PWV, photoplethysmography (PPG) and its pulse shape indices have been used to characterize differences between younger and elderly populations8. However, it has been suggested that the use of peripheral arterial stiffness indices may not be optimal for assessing cerebrovascular health, as peripheral sympathetic driven arterial wall muscle tone can be markedly different from cerebral arterial tone9. Thus, use of functional near-infrared spectroscopy (fNIRS) and optical diffusion imaging to directly assess the status of cerebrovascular impulses status from the brain impulses is of importance and has been increasingly studied. Traditionally, in fNIRS studies cardiac pulsation is filtered from the input signals when conducting slower 3–5 s analy","cbCaid4E8ftAEZ1X","https://ap.wps.com/l/cbCaid4E8ftAEZ1X","pdf",3672196,3,1,15,"English","en",105,"# Introduction\n## Ageing and brain health risk\n## Optical and fNIRS background\n## Arterial stiffness and existing assessments\n# Methods\n## Participants and study design\n## fNIRS signal processing and pulse extraction\n## Feature extraction and selection\n## Machine learning models and evaluation\n# Results\n## Classification performance and learning curves\n## Age-related information in pulse features\n# Discussion","[{\"question\":\"How were participants divided for the age group classification task?\",\"answer\":\"The study included thirty-six healthy adults and separated them into young (age ≤ 32) and elderly (age ≥ 57) groups.\"},{\"question\":\"What signal source and processing approach were used to extract brain pulses?\",\"answer\":\"Brain pulses were extracted from single-channel fNIRS using a single 830 nm wavelength, with pulse decomposition producing log-normal parameter-based feature sets.\"},{\"question\":\"Which machine learning models and feature selection methods were tested?\",\"answer\":\"Support vector machines and random forest learners were used, with feature selection performed using mRMR and principal component analysis. Performance was assessed across increasing sample sizes using learning curves.\"}]","Age group classification based on optical measurement of brain pulsation using machine learning | PDF",1785945679,38,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"age-group-classification-based-on-optical-measurement-of-brain-pulsation-using-machine-learning","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/age-group-classification-based-on-optical-measurement-of-brain-pulsation-using-machine-learning/128213/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"How were participants divided for the age group classification task?","Question",{"text":76,"@type":77},"The study included thirty-six healthy adults and separated them into young (age ≤ 32) and elderly (age ≥ 57) groups.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What signal source and processing approach were used to extract brain pulses?",{"text":81,"@type":77},"Brain pulses were extracted from single-channel fNIRS using a single 830 nm wavelength, with pulse decomposition producing log-normal parameter-based feature sets.",{"name":83,"@type":74,"acceptedAnswer":84},"Which machine learning models and feature selection methods were tested?",{"text":85,"@type":77},"Support vector machines and random forest learners were used, with feature selection performed using mRMR and principal component analysis. Performance was assessed across increasing sample sizes using learning curves.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]