High performance EEG feature extraction for fast epileptic seizure detection
Bibliographic record
Abstract
Epilepsy is a neurological disorder that affects around 70 million people worldwide. Early detection of epileptic seizures has the potential to help patients in improving their quality of life. Electroencephalogram (EEG) has been used to record the brain's electrical activities associated with seizures. This paper presents a fast method for selecting EEG features that are relevant to early detection of epileptic seizures. The feature extraction model is based on LASSO regression and is applied to the EEG spectrum to recognize the EEG spectral features pertinent to seizures. These features are then selected and fed into a random forest (RF) classifier for epileptic seizure recognition. Compared to the state-of-the-art methods, the proposed scheme achieves the highest detection performance of 100% sensitivity, 100% specificity, 100% classification accuracy, and 1.18 Sec detection delay. Furthermore, our model has proven to be robust in noisy and abnormal conditions.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".