Diagnosing Epilepsy from EEG Using Machine Learning and Welch Spectral Analysis
Bibliographic record
Abstract
Epilepsy is a neurological disorder that is characterized by recurring seizures.Seizures are electrical disturbances in the brain that develop suddenly and uncontrollably.They can cause various symptoms, depending on what part of the brain is affected.The cause of epilepsy is often unknown, but it can be caused by brain injury, brain infections, genetics, or other medical conditions.EEG analysis is a very important aspect of the diagnosis and treatment of epilepsy.It includes the interpretation of electrical activity patterns recorded from the electrodes.In this study, the machine learning methods and deep learning methods have been examined for epilepsy diagnosis.Random Forest (RF), Naive Bayes (NB) algorithm, Support Vector Machine (SVM), Levenberg-Marguardt (LM), and Long Short Term Memory (LSTM) were used for classification, while the Welch method has been used for feature extraction.The Bonn EEG dataset has been used for application.As a result, the RF method showed the best accuracy as 99.87%.RF achieved 99.84% precision, 99.9% sensitivity, 99.87% F1-Score, and 99.87 AUC.LSTM achieved the second accuracy degree as 99.39%.LSTM achieved 99.52% precision, 99.29% sensitivity, 99.39% F1-Score, and 99.40 AUC.LM, SVM, and NB achieved 98.82%, 97.90%, and 97.66% classification accuracies respectively.LM achieved 97.85% precision, 99.97% sensitivity, 98.87% F1-Score, and 98.92 AUC.SVM achieved 96.10% precision, 100% sensitivity, 97.99% F1-Score, and 98.10 AUC.NB achieved 98.80% precision, 96.42% sensitivity, 97.27% F1-Score, and 97.61 AUC.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".