Exploring the Efficacy of Explainable Deep Learning in Identifying Neuromarkers for Precise Prediction of Epilepsy and Causal Connectivity Analysis
Bibliographic record
Abstract
Causal connectivity among the brain regions have been recently exploited information for discriminating epileptiforms to detect epileptic seizures. Published investigations to detect ictal, interictal, preictal EEG reported existence of long-range correlations of excitations within a functionally connected brain region and shifting of focus of excitations from one region to another region and increase or decrease of intensity in certain frequency-bands, which can be quantified using suitable measure of Granger causality (GC). Deep neural networks obviated explicit preprocessing and feature extraction. The proposed work employs temporal dilated convolutional network to estimate causal connectivity relations among brain-regions in various frequency-bands in distributed manner. It implicitly learns varying autoregressive-lag-orders using stacked layers and covers long range relationships using exponential update of layer-wise dilation-factor. Model training with several parameter-combinations were conducted over 10 subjects. The proposed model outperformed the existing approaches and baseline model in terms of accuracy, sensitivity, and false positive rate. Class-wise dominating features were obtained using statistical significance analysis followed by family wise error rate correction using Benjamini-Hochberg method.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".