Optimal Evaluation of Digital Electroencephalograms
Bibliographic record
Abstract
Currently available digital EEG equipment provides considerably greater opportunities for clinical data analysis than is generally appreciated especially when appropriate software is used. Data from 7 different laboratories that had been obtained for routine diagnostic evaluations on 7 different EEG instruments and stored on compact disks were investigated. Since the instruments do not filter the data at input, ultra slow activity down to 0.01 Hz is currently being recorded but the attenuation factor is instrument dependent. Nevertheless, relevant clinical information is potentially available in these data and needs to be explored. Several examples in regard to epilepsy are presented. Determination of seizure onset may depend on the frequencies that are examined. The use of appropriate filter settings and viewing windows for the clinical question to be answered is stressed. Differentiation between simple and complex spike wave discharges, as well as spread of spikes, can readily be achieved by expanding the time base to 1 or 2 seconds and placing a cursor on the peak of the negative spike. Latencies in the millisecond range can then become apparent. EEGs co-registered with MEG should be evaluated with the same software in order to allow an adequate assessment of the similarities and differences between electrical and magnetic activity. An example of a comparison of EEG, planar gradiometers and magnetometers for an averaged spike is shown.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".