Accurate Classification of Seizure and Seizure-Free Intervals of Intracranial EEG Signals From Epileptic Patients
Why this work is in the frame
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Bibliographic record
Abstract
Electroencephalogram (EEG) signals are widely used to detect epileptic seizures in a patient's neuronal activity. Since visual inspection and interpretation of EEG signal are time-consuming and prone to errors, various computer-aided diagnostic (CAD) tools have been proposed. In this paper, we present a novel automated detection system to distinguish between intracranial EEG time courses with seizures and those that are seizure-free based on complexity measures. Specifically, the features used to characterize the EEG signals are estimates of multiscaling properties over a large spectrum measured by using the generalized Hurst exponent. We tested the capacity of these estimates to correctly classify seizure intervals using a publicly available data set. Using the k-nearest neighbor classifier and testing with tenfold cross validation, we achieved 100% accurate classification. Our proposed CAD system outperformed the existing state-of-the-art models. Moreover, our CAD system is not only accurate but also fast and simple to implement. Therefore, it can be used as an expert system to support a decision in clinical applications.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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