Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/11778
Title: Identification of Neurological Markers of Sarcopenia Disease Using Functional Near-Infrared Spectroscopy and Machine Learning
Other Titles: Sarkopeni Hastalığının Nörolojik Belirteçlerinin İşlevsel Yakın Kızılaltı Spektroskopisi ve Makine Öğrenmesi Kullanılarak Belirlenmesi
Authors: Şahin, B.M.
Şanlı, S.
Erdoğan, K.
Durmuş, M.E.
Kara, Ö.
Kaymak, B.
Kara, M.
Keywords: fNIRS
Machine Learning
Neurocognition
Sarcopenia
Classification (of information)
Learning systems
Muscle
Support vector machines
FNIRS
Functional near infrared spectroscopy
Machine-learning
Muscle function
Muscle mass
Muscle strength
Neurocognition
Performance
Quality of life
Sarcopenia
Biomarkers
Publisher: Institute of Electrical and Electronics Engineers Inc.
Abstract: Sarcopenia, a disease defined by the loss of muscle mass and function, plays a significant role in the quality of life of the elderly. Recent studies suggest that the loss of muscle strength and function associated with sarcopenia may be linked to neural control mechanisms. This study aimed to find a neurocognitive biomarker for sarcopenia and to classify it using fNIRS and machine learning methods. Connectivity matrices created from fNIRS data obtained from the Hand Grip experiment, conducted on 50 participants (27 controls, 23 sarcopenic), were used as features in the classification. This resulted in the Linear SVM model showing the highest performance with an 87.4% accuracy rate and 0.94 AUC value. These results indicate that functional connectivity data obtained through fNIRS could serve as an objective biomarker for sarcopenia classification, and that high-performance classification is feasible using this biomarker. © 2024 IEEE.
Description: Berdan Civata B.C.; et al.; Figes; Koluman; Loodos; Tarsus University
32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 -- 15 May 2024 through 18 May 2024 -- Mersin -- 201235
URI: https://doi.org/10.1109/SIU61531.2024.10600840
https://hdl.handle.net/20.500.11851/11778
ISBN: 979-835038896-1
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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