Abstract TP56: Low Cost, Portable Electroencephalograph May Improve The Accuracy Of Prehospital Stroke Diagnosis And Detection Of Large Vessel Occlusion
Bibliographic record
Abstract
Accurate and timely prehospital stroke diagnosis and detection of large vessel occlusion (LVO) are essential to ensure stroke patients are transported to hospitals that offer emergent reperfusion therapies. However, symptom based prehospital stroke scales often fail to identify LVO. Thus, a need exists for cost-effective and portable diagnostic tools, such as portable electroencephalography (EEG) to improve the accuracy of prehospital stroke diagnosis. Hypotheses: 1) Quantitative EEG measures will differ between LVO and non-LVO stroke patients, particularly in regards to brain slowing (ratio of low to high frequency oscillatory brain power) and brain asymmetry (ratio between oscillations in the affected and unaffected hemisphere) 2) Combining EEG with prehospital stroke scales will improve the accuracy of LVO detection. We enrolled patients with acute suspected stroke on presentation to an emergency department at a comprehensive stroke centre. Patients were rapidly evaluated with the Los Angeles Motor Scale followed by a 3-minute resting-state EEG recording using a modified Muse EEG headband (InteraXon). The LVO diagnosis and the extent of cerebral blood flow abnormalities were determined from CT angiography and CT perfusion imaging performed in close temporal proximity to the EEG recording. The study enrolled 74 patients (n= 8 LVO, n=66 non-LVO, including stroke mimics). Initial analysis suggests that LVO patients have trends towards brain slowing, as measured by the delta alpha ratio (LVO: mean = 1.21, SEM = 0.03; non-LVO: mean = 1.19, SEM = 0.01; p-value = 0.34). Additionally, LVO patients showed a trend towards increased brain asymmetry from 6-8 Hz, suggesting physiological differences between hemispheres specific to the theta frequency (LVO: mean = 0.02, SEM = 0.006; non-LVO: mean = 0.01, SEM = 0.002; p-value = 0.13). Quantitative measures will be assessed using classification trees to determine which combination of EEG and clinical features is most predictive of LVO. In conclusion, acute differences in brain activity between LVO and non-LVO patients can be detected with portable EEG, which when combined with clinical stroke scales, have the potential to improve the diagnosis and triage of suspected stroke patients in a prehospital setting.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".