A Novel Method to Assist Clinical Management of Mild Traumatic Brain Injury by Classifying Patient Subgroups Using Wearable Sensors and Exertion Testing: A Pilot Study

Simple item page
aut.relation.endpage249
aut.relation.issue2
aut.relation.journalBiomechanics
aut.relation.startpage231
aut.relation.volume3
dc.contributor.authorMcGeown, Joshua P
dc.contributor.authorPedersen, Mangor
dc.contributor.authorHume, Patria A
dc.contributor.authorTheadom, Alice
dc.contributor.authorKara, Stephen
dc.contributor.authorRussell, Brian
dc.date.accessioned2023-06-02T04:05:07Z
dc.date.available2023-06-02T04:05:07Z
dc.date.issued2023-05-26
dc.description.abstractAlthough injury mechanisms of mild traumatic brain injury (mTBI) may be similar across patients, it is becoming increasingly clear that patients cannot be treated as one homogenous group. Several predominant symptom clusters (PSC) have been identified, each requiring specific and individualised treatment plans. However, objective methods to support these clinical decisions are lacking. This pilot study explored whether wearable sensor data collected during the Buffalo Concussion Treadmill Test (BCTT) combined with a deep learning approach could accurately classify mTBI patients with physiological PSC versus vestibulo-ocular PSC. A cross-sectional design evaluated a convolutional neural network model trained with electrocardiography (ECG) and accelerometry data. With a leave-one-out approach, this model classified 11 of 12 (92%) patients with physiological PSC and 3 of 5 (60%) patients with vestibulo-ocular PSC. The same classification accuracy was observed in a model only using accelerometry data. Our pilot results suggest that adding wearable sensors during clinical tests like the BCTT, combined with deep learning models, may have the utility to assist management decisions for mTBI patients in the future. We reiterate that more validation is needed to replicate the current results.
dc.identifier.citationBiomechanics, ISSN: 2673-7078 (Online), MDPI AG, 3(2), 231-249. doi: 10.3390/biomechanics3020020
dc.identifier.doi10.3390/biomechanics3020020
dc.identifier.issn2673-7078
dc.identifier.urihttps://hdl.handle.net/10292/16214
dc.languageen
dc.publisherMDPI AG
dc.relation.urihttps://www.mdpi.com/2673-7078/3/2/20
dc.rights.accessrightsOpenAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleA Novel Method to Assist Clinical Management of Mild Traumatic Brain Injury by Classifying Patient Subgroups Using Wearable Sensors and Exertion Testing: A Pilot Study
dc.typeJournal Article
pubs.elements-id507568
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
McGeown et al._2023_ A Novel Method to Assist Clinical Management.pdf
Size:
3.99 MB
Format:
Adobe Portable Document Format
Description:
Journal article
Download
Collections
School of Sport and Recreation - Te Kura Hākinakina