Automatic localization of epileptic zones using magnetoencephalography.
Bibliographic record
Abstract
Conventional visual identification of epileptic spike is a challenging problem in the clinical application of magnetoencephalography (MEG). More importantly, the conventional method has problems of detecting other abnormalities such as high frequency oscillation in the human epileptic brain. The objective of this study was to develop a new approach using magnetic spectral analysis and spatial filtering. Twelve patients with seizure have been studied with a whole cortex MEG system. Fifteen epochs were recorded for each patient; each epoch was 120 seconds. Neuromagnetic spectrum was analyzed using a new method called accumulated spectrogram. Focal increases of spectral power were localized using synthetic aperture magnetometry (SAM). The MEG results were then compared with clinical findings. Focal increases of spectral power have been identified in all patients (12/12, 100%). The locations of the focal increases of spectral power were in agreement with dipole locations of spikes in 9 patients (9/12, 75%). A comparison between MEG results and clinical findings indicated that SAM revealed focal epileptic activities in two patients when dipole fitting failed. The results suggest that epileptic regions could be quantitatively identified and accurately localized using accumulated spectrogram and SAM. In comparison to visual identification of spike, the new approach is objective and sensitive, and provides the possibility of analyzing much wider frequency bands.
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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 | 0.001 |
| 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) | 0.001 | 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".