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Noninvasive Nonlinear Method for Seizure Prediction

Details

Project TitleNoninvasive Nonlinear Method for Seizure Prediction
Track CodeP17713
Short DescriptionUM File # 2377

Background
Epilepsy affects approximately 2.5 million individuals in the US, with about 150,000-200,000 new cases diagnosed per year. In approximately 75% of affected individuals, the epilepsy has no identifiab
AbstractNoninvasive Nonlinear Method for Seizure Prediction
 
TagsCNS & Neurosciences, diagnostics
 
Posted DateFeb 1, 2012

Summary

Noninvasive Nonlinear Method for Seizure Prediction

Description

UM File # 2377

Background
Epilepsy affects approximately 2.5 million individuals in the US, with about 150,000-200,000 new cases diagnosed per year. In approximately 75% of affected individuals, the epilepsy has no identifiable cause. An epileptic incident is characterized by intermittent interruption of normal brain function by sudden and often intense periods of synchronous neural discharge, resulting in either convulsive seizures, or more subtle alterations in neurological function such as brief lapses in full consciousness. To date there is essentially no understanding of why a particular patient will have a seizure at any point in time. As a consequence, the prediction and treatment of epileptic seizures in individual patients remains very challenging.

Technology Description
Researchers at the University of Michigan have developed methods and devices for noninvasive nonlinear prediction of ictal onset in patients afflicted by epilepsy. The approach is based on analysis of electroencephalogram (EEG) recordings, one set of recordings taken from an electrode close to the region of ictal onset, and a second or more set of recordings (e.g., concurrent readings) taken from a region remote from the region of ictal onset. The respective EEG recordings are converted into time series. A nonlinear quantity called the marginal predictability (MT) is calculated for each of the time series generated and values compared. The difference between the two MP values predictably decreases prior to ictal onset. During the preictal period, the patient may be warned of the potential oncoming seizure.

Applications
• prediction of ictal onset in epilepsy patients

Advantages
• noninvasive method to alert patient of potential oncoming seizure

Contact Information

Rakhi Juneja
rjuneja@umich.edu