Related Experiment Video
Updated: Jul 23, 2025
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
Published on: April 13, 2013
Sensor-level MEG combined with machine learning yields robust classification of mild traumatic brain injury patients
Juho Aaltonen1, Verna Heikkinen1, Hanna Kaltiainen2
1BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital, Helsinki University and Aalto University School of Science, P.O. Box 340, 00029 HUS Helsinki, Finland; Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Aalto University, P.O. Box 12200, 00760 AALTO, Finland.
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.
