Abstract TMP30: Combining the Los Angeles Motor Scale and the Muse Portable Electroencephalography System Improves the Accuracy of Large Vessel Occlusion Detection in Acute Stroke Syndrome.
Bibliographic record
Abstract
Background: The prehospital scales have been developed to identify stroke patients with large vessel occlusion (LVO) to facilitate rapid transport to appropriate stroke centres. In practice, these stroke scales have moderate accuracy. There is a pressing need for adjunct easy-to-use and interpret diagnostic devices to improve prehospital stroke diagnosis and LVO detection. We aim to determine whether a machine learning algorithm using adjunct electroencephalography (EEG) Spectra can improve the accuracy of LVO detection Methods: Adult patients with suspected acute stroke were prospectively enrolled as soon as possible on arrival at the emergency department. A wearable Muse TM headband (InteraXon Inc, Canada) with an embedded 4-channel EEG was used for a resting 3-minute recording. EEG Spectra including relative alpha, beta, theta and delta spectral powers, delta-alpha ratio (DAR) and pairwise-derived brain symmetry indices (pdBSI) were calculated. These indices were compared between patients with LVO and non-LVO groups. The accuracy of LVO detection was tested with the aid of supervised machine learning(ML) algorithms including EEG Spectra, Los Angeles Motor Stroke Scale (LAMS), sex and side of stroke. Results: A total of 142 patients were included in the analysis with a mean age of 69.6±13.7 years, 60(42.2%) females, (Stroke Subtype:113[79.6%] were ischemic stroke, 22[15.5%] stroke mimics, 7[4.9%] intracerebral hemorrhage) and median NIHSS 5(2-11). Thirty-seven(26.1%) patients had LVO and EEG was acquired at a median of 6h 45m (3h 29m - 14h 15m) after symptom onset. Relative alpha spectral power was lower in both affected (p<0.0001) and unaffected hemispheres (p<0.0001) in the LVO group (Figure 1); there was no difference in the median affected hemisphere DAR (p=0.4). However, the median unaffected hemisphere DAR was higher in the LVO group compared to the no-LVO group (p=0.03) (Figure 2). The Support vector machine-based ML algorithm accuracy for detecting LVO was: 0.6 for clinical assessment (LAMS+ Side of deficit+ Sex) alone, 0.73 for EEG Spectra alone and 0.93 for clinical assessment + EEG Spectra (0.9 Sensitivity; 0.95, Positive Predictive value) Conclusion: Combining QEEG with clinical assessment significantly improves the overall accuracy of LVO detection versus LAMS alone in patients presenting with acute stroke syndrome. Future studies are ongoing to determine if a short EEG acquisition in the prehospital phase is useful for rapid triage.
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 | 0.001 | 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) | 0.002 | 0.001 |
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".