Toward Reliable Models for Distinguishing Epileptic High-Frequency Oscillations (HFOs) from Non-HFO Events Using LSTM and Pre-Trained OWL-ViT Vision–Language Framework
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Bibliographic record
Abstract
Background: Over the past two decades, high-frequency oscillations (HFOs) between 80 and 500 Hz have emerged as valuable biomarkers for delineating and tracking epileptogenic brain networks. However, inspecting HFO events in lengthy EEG recordings remains a time-consuming visual process and mainly relies on experienced clinicians. Extensive recent research has emphasized the value of introducing deep learning (DL) and generative AI (GenAI) methods to automatically identify epileptic HFOs in iEEG signals. Owing to the ongoing issue of the noticeable incidence of spurious or false HFOs, a key question remains: which model is better able to distinguish epileptic HFOs from non-HFO events, such as artifacts and background noise? Methods: In this regard, our study addresses two main objectives: (i) proposing a novel HFO classification approach using a prompt engineering framework with OWL-ViT, a state-of-the-art large vision–language model designed for multimodal image understanding guided by optimized natural language prompts; and (ii) comparing a range of existing deep learning and generative models, including our proposed one. Main results: Notably, our quantitative and qualitative analysis demonstrated that the LSTM model achieved the highest classification accuracy of 99.16% among the time-series methods considered, while our proposed method consistently performed best among the different approaches based on time–frequency representation, achieving an accuracy of 99.07%. Conclusions and significance: The present study highlights the effectiveness of LSTM and prompted OWL-ViT models in distinguishing genuine HFOs from spurious non-HFO oscillations with respect to the gold-standard benchmark. These advancements constitute a promising step toward more reliable and efficient diagnostic tools for epilepsy.
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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 | 0.001 |
| 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