Early Epileptic Seizure Prediction Using EEG Signals with Machine Learning
Bibliographic record
Abstract
Epilepsy is a chronic disease that dates back to ancient times and affects people only during seizures.Since the onset of seizures is unknown, it heavily poor affects the living standards of patients.If seizure onset can be predicted in sufficient advance, seizures can be prevented with drugs to be used or an opportunity can be provided for patients who cannot be stopped with drugs to move to a safe zone.For this purpose, to predict an epileptic seizure, before a certain period of time happens, frequency-based feature extraction is applied with the use of recorded EEG data.Bases of the study rely on creating time for patients to reach necessary medications approximately ahead 30-60 minutes before having an epileptic seizure.In this respect, an open-access dataset with 24 pediatric patients' EEG recordings was used and frequency-based feature extraction was performed using wavelet transformation.Afterward, classification performances of the features are compared for a k-nearest neighbor (k-NN), random forest algorithm (RF), support vector machine (SVM), and J48 which are extensively used machine learning techniques.In accordance with the classification results, the average highest accuracy was acquired as 99.87% with the SVM classifier.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".