Method for automatic and unsupervised classification of high-frequency oscillations in physiological recordings. 

Automatic detection and classification of high frequency oscillations (HFO) data for use in clinical diagnosis and therapy has been limited. Currently, researchers visually inspect for high frequency oscillations. 

An algorithm to automatically extract quantitative local seizure information from multielectrode data. The automated HFO detection and classification algorithm is comprised of three major stages: 
  -  In the first stage, candidate HFO events are detected in the band-pass filtered iEEG.
  -  In the second stage, a statistical model of the local background iEEG surrounding each candidate event is built. Events bearing too large a spectral similarity to the background activity according to the model are discarded from candidacy.
  -  In the final stage, computational features are extracted from the retained candidates and these features are used, after a dimensionality reduction step, as inputs to a classifier.

• Algorithm may process brain oscillations within the 100-500 Hz frequency range, action potentials and epileptic spikes, and other brain/body generated signals
• Algorithm may be implemented in intracranial electroencephalographic monitoring systems or brain-implantable devices
• Algorithm may enable quantitative seizure localization in devices

• Intracranial electroencephalographic (iEEG) monitoring systems used in surgical evaluation for epilepsy patients who are unresponsive to antiepileptic drugs
• Brain-implantable devices that monitor, predict, abort, and/or control epileptic seizures by using the feedback to deliver a therapy