PyHFO 2.0: An Open-Source Platform for Deep Learning–Based Clinical High-Frequency Oscillations Analysis
Bibliographic record
Abstract
Abstract Accurate detection and classification of high-frequency oscillations (HFOs) in electroencephalography (EEG) recordings have become increasingly important for identifying epileptogenic zones in patients with drug-resistant epilepsy. However, few open-source platforms offer comprehensive and accessible tools that integrate conventional signal processing with modern deep learning approaches for biomarker analysis. We introduce PyHFO 2.0, an enhanced platform designed for automated detection, classification, and expert annotation of neural events. PyHFO 2.0 includes three commonly used detection methods: short-term energy (STE), the Montreal Neurological Institute (MNI) approach, and a Hilbert transform-based detector. For classification, the platform incorporates deep learning models for artifact rejection, spike-associated HFO (spkHFO) detection, and epileptogenic HFO (eHFO) identification. These models are integrated with the Hugging Face ecosystem for seamless loading and can be replaced with custom-trained alternatives. Furthermore, PyHFO 2.0 features an interactive annotation interface that enables clinicians and researchers to inspect, verify, and refine automated results. The platform was validated using clinical EEG datasets from both human and rodent models of epilepsy, confirming its reliability. PyHFO 2.0 aims to simplify the use of computational neuroscience tools in both research and clinical environments by combining methodological rigor with a user-friendly graphical interface. Its scalable architecture and model integration capabilities support a range of applications in biomarker discovery, epilepsy diagnostics, and clinical decision support, bridging advanced computation and practical usability.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.010 |
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".