Multiclass Classification of Epileptic Seizure Using Machine Learning
Bibliographic record
Abstract
Epileptic seizures are neurological disorders instigated by sudden, uncontrolled electrical disturbances and activities in the brain, leading to changes in behavior, movements, or feelings, and, in some cases, loss of consciousness.Electroencephalogram (EEG) signals are utilized in the medical field to diagnose epileptic seizures.For effective management and treatment of patients, accurate and timely detection of these seizures is crucial.The paper presents a robust machine learning system to do multiclass classification of epileptic seizures using EEG data.The study uses datasets from Bonn University, which include five different sets representing various brain states of healthy and epileptic individuals.After preprocessing and normalizing the data, the features are extracted using techniques like power spectral density (PSD) and wavelet transforms.Various classification algorithms like Decision Tree, Random Forest, Naï ve Bayes, and Support Vector Machine were evaluated through extensive hyperparameter tuning and cross-validation.The Random Forest model emerged as the best classifier, achieving a significant accuracy of 89% in classifying the data into five classes, showing its effectiveness in distinguishing between different classes of seizures.This approach shows significant promise for enhancing the accuracy of epilepsy classification and optimizing treatment strategies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".