Iuliano, Sofia (2024) Evaluating EEG as a prognostic tool to predict persistent symptoms in adolescents with concussion. Masters thesis, Concordia University.
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Abstract
Approximately 30% of children with concussion experience persistent post-concussion symptoms (PPCS), but no accurate prognostic tools are available. Electroencephalography (EEG) identifies alterations in electrical activity post-concussion, but its prognostic capability is unknown. Our objective was to determine if EEG outperforms current prognostic tools in predicting PPCS at 1-month post-concussion. Thirty-four adolescents (12.9 +/- 2.2 years, 20 males) with concussion were recruited. The treating physician predicted the likelihood of PPCS (0-100) at diagnosis in the emergency room. Participants completed a resting-state EEG and a brief clinical assessment battery 6.4 +/- 2.4 days post-injury and the Post-Concussion Symptom Inventory (PCSI) 28.9 +/- 1.9 days post-injury. PPCS (yes/no) was defined as an increase of ≥7 points on the PCSI compared to pre-injury symptom ratings. Twelve (35.3%) participants experienced PPCS at 1-month post-injury. Independent t-tests found that F3 delta power (p=0.04), F4 delta power (p=0.1), and F4 theta power (p=0.07) differed in adolescents with and without PPCS, which were combined into a multivariable model. Using inferential approaches, the EEG model had an AUC=0.71 (sensitivity=75%, specificity=68.2%) compared to the 5P Score (AUC=0.66, sensitivity=75%, specificity=45.9%) and physician prediction (AUC=0.55, sensitivity=71.4%, specificity=38.9%) models. However, these differences were not statistically significant (p=0.60 and p=0.32 respectively). The optimal machine learning model (SVM radial kernel, C=0.2) found that only the EEG performed significantly better than random chance in the training (72.9% accuracy, p<0.001) and validation set (62%, p<0.001). EEG features have potential as a prognostic biomarker of PPCS. Future studies should include larger samples and different EEG systems and features.
| Divisions: | Concordia University > Faculty of Arts and Science > Health, Kinesiology and Applied Physiology |
|---|---|
| Item Type: | Thesis (Masters) |
| Authors: | Iuliano, Sofia |
| Institution: | Concordia University |
| Degree Name: | M. Sc. |
| Program: | Health and Exercise Science |
| Date: | 3 July 2024 |
| Thesis Supervisor(s): | Teel, Elizabeth Fay |
| ID Code: | 994392 |
| Deposited By: | Sofia Iuliano |
| Deposited On: | 24 Oct 2024 17:20 |
| Last Modified: | 24 Oct 2024 17:20 |
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