Age group classification based on optical measurement of brain pulsation using machine learning

• Published in: Scientific reports Vol. 15; no. 1; pp. 3166 - 15
• Main Authors: Ilvesmäki, Martti, Ferdinando, Hany, Noponen, Kai, Seppänen, Tapio, Korhonen, Vesa, Kiviniemi, Vesa, Myllylä, Teemu
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 25.01.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166/985; 639/624/1075; Accuracy; Adult; Age Factors; Age groups; Aged; Aging; Aging - physiology; Brain - blood supply; Brain - diagnostic imaging; Brain - physiology; Brain research; Cerebrovascular Circulation - physiology; Classification; Feature selection; Female; Functional magnetic resonance imaging; Geriatrics; Hemodynamics; Humanities and Social Sciences; Humans; Infrared spectroscopy; Learning algorithms; Learning curves; Machine Learning; Magnetic Resonance Imaging; Male; Measurement techniques; Middle Aged; multidisciplinary; Neuroimaging; Older people; Principal components analysis; Sample size; Science; Science (multidisciplinary); Spectroscopy, Near-Infrared - methods; Spectrum analysis; Support Vector Machine; Veins & arteries; Young Adult

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 t...