Data-Driven Model for Improving MEG Epileptic Spike Detection
Bibliographic record
Abstract
Epilepsy is a neurological disorder characterized by spontaneous recurrent seizures, affecting over 50 million people worldwide. The prompt and accurate detection of epileptic events is crucial for effective treatment and management. Traditional methods such as Electroencephalogram (EEG) have been complemented by Magnetoencephalography (MEG), which offers superior spatial resolution due to its insensitivity to the distortive effects of the skull and scalp. This study advances the analysis of MEG data using Complex Continuous Morlet Wavelet Transform (CCMWT) for feature extraction, coupled with innovative machine learning architectures for classifying epileptic versus non-epileptic signal segments. We developed and compared a 3D Convolutional Neural Network (3D-CNN) and a VGG16 model with the Transfer learning strategy, in terms of accuracy, computational efficiency, and error rates. Our results demonstrate the VGG16 model with the Transfer learning strategy's superior performance in all cases except the computation times.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".