Application of the Muse portable EEG system to aid in rapid diagnosis of stroke
Bibliographic record
Abstract
Abstract Objective In this pilot study, we investigated using portable electroencephalography (EEG) as a potential prehospital stroke diagnostic method. Methods We used a portable EEG system to record data from 25 participants, 16 had acute ischemic stroke events, and compared the results of age-matched controls that included stroke mimics. Delta/alpha ratio (DAR), (delta+theta)/(alpha+beta) ratio (DBATR) and pairwise-derived Brain Symmetry Index (pdBSI) were investigated, as well as accelerometer and gyroscope trends. We then made classification trees using TreeBagger to distinguish between different subgroups. Results DAR and DBATR showed an increase in ischemic stroke patients that correlates with stroke severity (p<0.01, partial η 2 = 0.293; p<0.01, partial η 2 = 0.234). pdBSI decreased in low frequencies and increased in high frequencies in patients who had a stroke (p<0.05, partial η 2 = 0. 177). All quantitative EEG measures were significant between stroke patients and controls. Using classification trees, we were able to distinguish between subgroups of stroke patients and controls. Conclusions There are significant differences in DAR, DBATR, and pdBSI between patients with ischemic stroke when compared to controls; results relate to severity. Significance With significant differences between patients with strokes and controls, we have shown the feasibility and utility for the Muse™ EEG system to aid in patient triage and diagnosis as an early detection tool.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.003 | 0.000 |
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".