Key result
PyHFO 2.0 successfully integrated deep learning-based classification with conventional signal processing, achieving an accuracy of 96.4% for artifact classification and 90.5% for spike classification.
Why the study?
Few open-source platforms offer comprehensive and accessible tools integrating conventional signal processing with deep learning for high-frequency oscillation analysis in drug-resistant epilepsy.
PyHFO 2.0 provides an open-source, user-friendly platform integrating deep learning and conventional signal processing for accurate detection and classification of high-frequency oscillations in EEG.
May enable efficient HFO detection on limited hardware; leaves open prospective clinical validation before routine use.
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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Ding et al. (2025) studied Drug-resistant focal epilepsy (n=39). PyHFO 2.0 software platform vs. RIPPLELAB and original deep learning models was evaluated on Accuracy of the artifact classification model on Hilbert-detected events. PyHFO 2.0 successfully integrated deep learning-based classification with conventional signal processing, achieving an accuracy of 96.4% for artifact classification and 90.5% for spike classification.
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