Description:

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

 

Problem: 

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. 

 

Solution: 

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.

 

Inventor:

Brian Litt, MD, Department of Neurology

 

Advantages: 

• 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

 

Applications: 

• 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

 

 

Stage of Development: 

• Algorithm is developed
• Continuous long-term data from 9 patients has been used

 

Intellectual Property: 

Published U.S.  Patent Application US20120245481

 

Reference Media: 

Blanco, et al. 2010. “Unsupervised classification of high-frequency oscillations in human neocortical epilepsy and control patients.”  J Neurophysiol, 104(5) 2900-2912. 

 

Docket # X5675