Comments on parametric and non-parametric detection of epileptiform spike activity
Bibliographic record
Abstract
Parametric methods which will detect spikes in EEG signal have been suggested by many authors, and encouraging results seem to have been obtained. A main limitation is instrument complexity and most of the results have been obtained relatively slowly off line. The basic theoretical limitations of the approach apart from the factor of instrumentation do not seem to be fully recognized. In this paper we show\bulletthat the parametric method is not optimum in the Weiner sense.\bulletthat anamolous indications of spike activity are given when p, the number of filter parameters, is varied.\bulletthat the mean square error is not an indicator of performance.\bulletthat low p values, perhaps surprisingly, lead to better spike detection than high values. Non-parametric methods have been suggested by several authors and they show that significantly less instrumentation is required and on-line results can be obtained in real time using modest equipment. A 16-channel real-time spike monitor has been realized on an Apple II computer using a method attributed to Ninomija et al. The basic approach is to detect incidence of shapes, all of which could be actual spikes, and then in subsequent filtering stages remove those shapes which do not meet the criteria spelled out by the neurosurgeon.
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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 | 0.016 | 0.132 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.028 | 0.023 |
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".