Feature Selection Techniques for the Analysis of Discriminative Features in Temporal and Frontal Lobe Epilepsy: A Comparative Study
Why this work is in the frame
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Bibliographic record
Abstract
Background: Because about 30% of epileptic patients suffer from refractory epilepsy, an efficient automatic seizure prediction tool is in great demand to improve their life quality. Methods: In this work, time-domain discriminating preictal and interictal features were efficiently extracted from the intracranial electroencephalogram of twelve patients, i.e., six with temporal and six with frontal lobe epilepsy. The performance of three types of feature selection methods was compared using Matthews’s correlation coefficient (MCC). Results: Kruskal Wallis, a non-parametric approach, was found to perform better than the other approaches due to a simple and less resource consuming strategy as well as maintaining the highest MCC score. The impact of dividing the electroencephalogram signals into various sub-bands was investigated as well. The highest performance of Kruskal Wallis may suggest considering the importance of univariate features like complexity and interquartile ratio (IQR), along with autoregressive (AR) model parameters and the maximum (MAX) cross-correlation to efficiently predict epileptic seizures. Conclusion: The proposed approach has the potential to be implemented on a low power device by considering a few simple time domain characteristics for a specific sub-band. It should be noted that, as there is not a great deal of literature on frontal lobe epilepsy, the results of this work can be considered promising.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it