ArticleSports medicine - open2019
Objective Classification of mTBI Using Machine Learning on a Combination of Frontopolar Electroencephalography Measurements and Self-reported Symptoms.
Article in Sports medicine - open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Addressing class imbalance in traumatic brain injury prognostication: A survey of resampling approaches.AIMS neuroscience · 2026Review
- A deep learning-based approach to diagnose mild traumatic brain injury using audio classification.PloS one · 2022Article
- Safeguarding Athletes Against Head Injuries Through Advances in Technology: A Scoping Review of the Uses of Machine Learning in the Management of Sports-Related Concussion.Frontiers in sports and active living · 2022Article
- Phybrata Sensors and Machine Learning for Enhanced Neurophysiological Diagnosis and Treatment.Sensors (Basel, Switzerland) · 2021Article
- Evaluating Performance of EEG Data-Driven Machine Learning for Traumatic Brain Injury Classification.IEEE transactions on bio-medical engineering · 2021Article
- Classification of Non-Severe Traumatic Brain Injury from Resting-State EEG Signal Using LSTM Network with ECOC-SVM.Sensors (Basel, Switzerland) · 2020Article
- Physiological Vibration Acceleration (Phybrata) Sensor Assessment of Multi-System Physiological Impairments and Sensory Reweighting Following Concussion.Medical devices (Auckland, N.Z.) · 2020Article
- Detection of Moderate Traumatic Brain Injury from Resting-State Eye-Closed Electroencephalography.Computational intelligence and neuroscience · 2020Article
- Visual working memory deficits in undergraduates with a history of mild traumatic brain injury.Attention, perception & psychophysics · 2019Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe reliable diagnosis of a mild traumatic brain injury (mTBI) is a pervasive problem in sports and in the military. The frequency and severity of each occurrence, while difficult to quantify, may impact long term cognitive function and quality of life. Despite the new revelations concerning brain disfunction from head injuries, individuals still feel pressure to remain on the field despite a debilitating injury. In this study, we evaluated the accuracy of a system that could be employed on the sidelines or in the locker room to provide an immediate objective mTBI assessment.
methodsParticipants consisted of 38 individuals with a recent mTBI and 47 controls with no history of mTBI within the last 5 years. Participants were administered a simple symptom questionnaire, behavioral tests, and resting state EEG was measured using three frontopolar electrodes. An advanced machine learning algorithm called boosting was utilized to classify subjects into either injured or controls using power spectral densities on 1-min of resting EEG and the symptom questionnaire.
resultsResults based on leave-one-out cross-validation revealed that the addition of EEG measurements boosted the accuracy to approximately 91 ± 2% compared to 82 ± 4% from the symptom questionnaire alone.
conclusionThis study demonstrated the potential benefit of including EEG measurements to diagnose suspected brain injury patients. This is a step toward accurate and objective classification measurements that can be implemented on the field as a future injury assessment tool.
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