Epileptic Seizure Detection Using Convolution Neural Networks
Bibliographic record
Abstract
Epilepsy is a central nervous system disorder that affects a substantial number of world's population and disrupts the quality of life of the sufferers. A number of diagnostic techniques evolved over the years for the detection of epileptic seizures using encephalograms. The subject paper presents design and implementation of a classification model based on convolution neural networks that is capable of detecting epileptic seizures using computational methods utilizing encephalogram data. The study used convolution neural networks that have unique characteristics for recognizing patterns and images and in classifying their features. The neural network architecture proposed herein comprises of layers for input and output with several hidden convolution layers. The electroencephalogram database that was used in this work is the freely accessible CHB-MIT scalp encephalogram database. The developed approach was implemented using the 22 subject database and testing was carried out on patients a few days after the withdrawal of the anti-seizure medications. The test subjects were composed of 5 males and 17 females from various age groups. It was observed that the suggested algorithm could detect about 94.6 percent of the 198 tested seizure records, indicating a good performance of the proposed seizure detection algorithm.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".