PyHFO: lightweight deep learning-powered end-to-end high-frequency oscillations analysis application.

This study aims to develop and validate an end-to-end software platform, PyHFO, that streamlines the application of deep learning methodologies in detecting neurophysiological biomarkers for epileptogenic zones from EEG recordings.

Methods: We introduced PyHFO, which enables time-efficient HFO detection algorithms like short-term energy (STE) and Montreal Neurological Institute and Hospital (MNI) detectors. It incorporates deep learning models for artifact and HFO with spike classification, designed to operate efficiently on standard computer hardware. 

Main results: The validation of PyHFO was conducted on three separate datasets: the first comprised solely of grid/strip electrodes, the second a combination of grid/strip and depth electrodes, and the third derived from rodent studies, which sampled the neocortex and hippocampus using depth electrodes. PyHFO demonstrated an ability to handle datasets efficiently, with optimization techniques enabling it to achieve speeds up to 50 times faster than traditional HFO detection applications. Users have the flexibility to employ our pre-trained deep learning model or use their EEG data for custom model training.

Significance: PyHFO successfully bridges the computational challenge faced in applying deep learning techniques to EEG data analysis in epilepsy studies, presenting a feasible solution for both clinical and research settings. By offering a user-friendly and computationally efficient platform, PyHFO paves the way for broader adoption of advanced EEG data analysis tools in clinical practice and fosters potential for large-scale research collaborations.

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