Sensor-level MEG combined with machine learning yields robust classification of mild traumatic brain injury patients
Loading...
Access rights
openAccess
URL
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
Date
2023-09
Major/Subject
Mcode
Degree programme
Language
en
Pages
9
79-87
79-87
Series
Clinical Neurophysiology, Volume 153
Abstract
Objective: Diagnosis of mild traumatic brain injury (mTBI) is challenging despite its high incidence, due to the unspecificity and variety of symptoms and the frequent lack of structural imaging findings. There is a need for reliable and simple-to-use diagnostic tools that would be feasible across sites and patient populations. Methods: We evaluated linear machine learning (ML) methods’ ability to separate mTBI patients from healthy controls, based on their sensor-level magnetoencephalographic (MEG) power spectra in the subacute phase (<2 months) after a head trauma. We recorded resting-state MEG data from 25 patients and 25 age-sex matched controls and utilized a previously collected data set of 20 patients and 20 controls from a different site. The data sets were analyzed separately with three ML methods. Results: The median classification accuracies varied between 80 and 95%, without significant differences between the applied ML methods or data sets. The classification accuracies were significantly higher with ML than with traditional sensor-level MEG analysis based on detecting pathological low-frequency activity. Conclusions: Easily applicable linear ML methods provide reliable and replicable classification of mTBI patients using sensor-level MEG data. Significance: Power spectral estimates combined with ML can classify mTBI patients with high accuracy and have high promise for clinical use.Description
Funding Information: Hanna Renvall was supported by the Academy of Finland (grant number 321460), Finnish Cultural Foundation and Paulo Foundation, Hanna Kaltiainen by the Finnish Medical Foundation, and Riitta Salmelin by the Academy of Finland (grant number 315553) and the Sigrid Jusélius Foundation. Funding Information: We acknowledge the computational resources provided by the Aalto Science-IT project and the skilled help in the MEG measurements by Mr. Jari Kainulainen. Part of the present work has previously been submitted as a partial fulfillment of the first author's Master of Science Degree (Aaltonen J: Application of linear machine learning methods for the diagnosis of mild traumatic brain injuries, Aalto University 2022). Publisher Copyright: © 2023 International Federation of Clinical Neurophysiology
Keywords
Machine learning, Magnetoencephalography, Mild traumatic brain injury, Resting-state
Other note
Citation
Aaltonen, J, Heikkinen, V, Kaltiainen, H, Salmelin, R & Renvall, H 2023, ' Sensor-level MEG combined with machine learning yields robust classification of mild traumatic brain injury patients ', Clinical Neurophysiology, vol. 153, pp. 79-87 . https://doi.org/10.1016/j.clinph.2023.06.010