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Neurometric Analysis System


In 1977, John, et al. proposed that the Neurometric Method may aid in the differential diagnosis of a variety of subtle brain dysfunctions. The frequency composition of the normal healthy EEG recorded from every brain region is predictable and changes systematically with normal development and aging. Normative values have been statistically described and independently confirmed by researchers in Barbados, China, Cuba, Germany,  Japan, Korea, Mexico, the Netherlands, Sweden, Venezuela, and the United States.

The results of these investigations have confirmed that statistically significant deviations from normal neurometric values are found in high proportions of patients with psychiatric illness, developmental disorders, cerebrovascular disease, early stages of dementia, and mild head injury, but abnormal findings seldom occur in normally functioning individuals. Obtaining such objective biological information can be an invaluable adjunct to patient evaluation, diagnosis, treatment selection and monitoring.

Mathematical discriminant equations that characterize the EEG of normal individuals and various clinical groups have been constructed and independently replicated. Practitioners can now use Neurometrics to objectively assess the statistical similarity between the individual patient and these statistically-defined distinctive group profiles. The Neurometric Analysis System (AAS) was cleared for medical use by the U.S. Food and Drug Administration for the post-hoc statistical evaluation of the EEG under FDA 5 1 0(k) K974748 in July of 1998.

Neurometrics provides a sound, extensively tested data selection and analysis method, rigorously defined and accurate statistical evaluations relative to validated normals, and the largest clinical QEEG database in the world.

Extensive statistical tables of univariate and multivariate measures of absolute and relative power, power asymmetry and synchronization (coherence), and color-coded topographic brain maps help identify abnormal brain electrical activity in your patients. The unique multivariate indices combine selected EEG features across different brain regions to quanity the organization of relationships within the brain. These statistical descriptors are especially sensitive to psychiatric illnesses, often characterized by disturbances of brain organization rather than brain structures.


1  Taken from: Neurometric Analysis System - Demonstration Program, NxLink, Ltd, Copyright, 1999