Modified Gorilla Troops Optimization with Deep Learning Based Epileptic Seizure Prediction Model on EEG Signals
Bibliographic record
Abstract
Approximately 50 million people worldwide suffer from Epileptic Seizure (ES), a persistent neurological disorder that cannot spread from person to person.Electroencephalography (EEG) is a tool that is often used to identify and diagnose epilepsy by observing how the brain works.However, analyzing EEG recordings to identify epileptic activity can be difficult, time-consuming, and requires specialist expertise.However, a precise and early diagnosis of epilepsy is necessary to start anti-seizure medication treatment and reduce the risk of consequences from recurrent episodes.In this paper, a modified Gorilla Troops Optimization with a Deep Learning based ES Prediction model (MGTODL-ESP) using EEG signals is implemented.The proposed MGTODL-ESP model comprises two main processes: feature selection and prediction.The MGTODL-ESP model uses a modified gorilla troops optimization (MGTO) based feature selection algorithm to select the optimal subset of features.The MGTO-based Gated Recurrent Unit (GRU) model predicts different types of ES.Finally, the Grey Wolf Optimizer (GWO) algorithm was used to tune the parameters of the MGTODL model.The outline of the MGTO-ESP-based feature selection and Grey Wolf Optimizer (GWO)-based parameter tuning indicates the novelty of this research.A comprehensive empirical study was conducted using a benchmark CHB-MIT scalp EEG database from IEEE DataPort to investigate the improved prediction performance of the MGTODL-ESP model.A comparison of the different methods showed that the MGTODL-ESP approach was the most accurate, with an accuracy rate of 98.50%.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".