EEG seizure detection and epilepsy diagnosis using a novel variation of Empirical Mode Decomposition
Bibliographic record
Abstract
Epileptic seizure detection and epilepsy diagnosis based on feature extraction and classification using electroencephalography (EEG) signals is an important area of research. In this paper, we present a simple and effective approach based on signal decomposition, using a novel variation of the Empirical Mode Decomposition called Empirical Mode Decomposition-Modified Peak Selection (EMD-MPS). EMD-MPS allows time-scale based de-trending of signals, allowing signals to be separated directly into a de-trended component, and a trend, according to a frequency separation criterion. Features are extracted from the decomposed components, and a simple classifier, namely the 1-NN classifier is used for three classification tasks. The technique is tested on a publicly available EEG database, and a classification accuracy of 99% for epilepsy diagnosis task, and 100% and 98.2% for two seizure detection tasks is obtained. These results are better than, or comparable to previous results using the same EEG database, but have been obtained with a simpler and computationally fast signal analysis and classification method.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".