ArticleScientific reports2019
Neurophysiological Correlates of Concussion: Deep Learning for Clinical Assessment.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Fact or Fiction-Accelerometry Versus Self-Report in Adherence to Pediatric Concussion Protocols: Prospective Longitudinal Cohort Study.JMIR pediatrics and parenting · 2024Article
- Evaluation of interpretability methods for multivariate time series forecasting.Applied intelligence (Dordrecht, Netherlands) · 2022Article
- School performance in youth after a concussion.Frontiers in sports and active living · 2022Article
- Evaluating Performance of EEG Data-Driven Machine Learning for Traumatic Brain Injury Classification.IEEE transactions on bio-medical engineering · 2021Article
- Recurrent neural network-based acute concussion classifier using raw resting state EEG data.Scientific reports · 2021Article
- Elevated and Slowed EEG Oscillations in Patients with Post-Concussive Syndrome and Chronic Pain Following a Motor Vehicle Collision.Brain sciences · 2021Article
- Brain Vital Signs in Elite Ice Hockey: Towards Characterizing Objective and Specific Neurophysiological Reference Values for Concussion Management.Frontiers in neuroscience · 2021Article
- Can Deep Learning Hit a Moving Target? A Scoping Review of Its Role to Study Neurological Disorders in Children.Frontiers in computational neuroscience · 2021Article
- Deep Learning Recurrent Neural Network for Concussion Classification in Adolescents Using Raw Electroencephalography Signals: Toward a Minimal Number of Sensors.Frontiers in human neuroscience · 2021Article
- Subconcussive brain vital signs changes predict head-impact exposure in ice hockey players.Brain communications · 2021Article
- On the time-course of functional connectivity: theory of a dynamic progression of concussion effects.Brain communications · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Concussion has been shown to leave the afflicted with significant cognitive and neurobehavioural deficits. The persistence of these deficits and their link to neurophysiological indices of cognition, as measured by event-related potentials (ERP) using electroencephalography (EEG), remains restricted to population level analyses that limit their utility in the clinical setting. In the present paper, a convolutional neural network is extended to capitalize on characteristics specific to EEG/ERP data in order to assess for post-concussive effects. An aggregated measure of single-trial performance was able to classify accurately (85%) between 26 acutely to post-acutely concussed participants and 28 healthy controls in a stratified 10-fold cross-validation design. Additionally, the model was evaluated in a longitudinal subsample of the concussed group to indicate a dissociation between the progression of EEG/ERP and that of self-reported inventories. Concordant with a number of previous studies, symptomatology was found to be uncorrelated to EEG/ERP results as assessed with the proposed models. Our results form a first-step towards the clinical integration of neurophysiological results in concussion management and motivate a multi-site validation study for a concussion assessment tool in acute and post-acute cases.
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Registered trials
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