Virginia B Planz, Lauren Hatcher, Krupa K Patel-Lippmann, Erin A Cooke, Reed A Omary
Advancing learning equity through virtual radiology grand rounds
Clinical imaging
objective study aims develop validate endtoend software platform pyhfo streamlines application deep learning dl methodologies detecting neurophysiological biomarkers epileptogenic zones eeg recordingsapproach introduced pyhfo enables timeefficient highfrequency oscillation hfo detection algorithms like shortterm energy montreal neurological institute hospital detectors incorporates dl models artifact hfo spike classification designed operate efficiently standard computer hardwaremain results validation pyhfo conducted three separate datasets first comprised solely gridstrip electrodes second combination gridstrip depth electrodes third derived rodent studies sampled neocortex hippocampus using depth electrodes pyhfo demonstrated ability handle datasets efficiently optimization techniques enabling achieve speeds 50 times faster traditional hfo detection applications users flexibility employ pretrained dl model use eeg data custom model trainingsignificance pyhfo successfully bridges computational challenge faced applying dl techniques eeg data analysis epilepsy studies presenting feasible solution clinical research settings offering userfriendly computationally efficient platform pyhfo paves way broader adoption advanced eeg data analysis tools clinical practice fosters potential largescale research collaborations