Sensor-level MEG combined with machine learning yields robust classification of mild traumatic brain injury patients
Aaltonen, Juho (2024-03-05)
Sensor-level MEG combined with machine learning yields robust classification of mild traumatic brain injury patients
(05.03.2024)
Julkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty.
suljettu
Julkaisun pysyvä osoite on:
https://urn.fi/URN:NBN:fi-fe2024031311112
https://urn.fi/URN:NBN:fi-fe2024031311112
Tiivistelmä
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.
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.