Automated analysis and trending of the raw EEG signal.

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Bibliographic Details
Title:	Automated analysis and trending of the raw EEG signal.
Authors:	Anderson NR (AUTHOR), Wisneski KJ (AUTHOR)
Source:	American Journal of Electroneurodiagnostic Technology. Sep2008, Vol. 48 Issue 3, p166-191. 26p.
Abstract:	The electroencephalogram (EEG) equipment industry has recently been developing systems that display, not only the raw EEG signal, but also a transformed version of the signal that highlights critical features and can be viewed in a more user friendly manner. A computer automated analysis of the signal is a quantitative approach that can make precise temporal measurements of the signal features, perform digital filtering to allow for identification of specific components of the signal, and statistically analyze the resulting signal. These quantitative analyses have created the potential to decrease the time required for EEG reviewers, allow for seizures to be more accurately detected with a simpler metric, and prevent confusion of symptom detection, thus providing for a more effective and efficient diagnosis. Many companies have addressed this opportunity for development and designed systems, each with their own name and features. This article attempts to explain the techniques for signal transformation that are starting to see wide use and point out some of the benefits of this type of interpretation that have been identified in the literature. [ABSTRACT FROM AUTHOR]
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Database:	Psychology and Behavioral Sciences Collection

Description
Abstract:	The electroencephalogram (EEG) equipment industry has recently been developing systems that display, not only the raw EEG signal, but also a transformed version of the signal that highlights critical features and can be viewed in a more user friendly manner. A computer automated analysis of the signal is a quantitative approach that can make precise temporal measurements of the signal features, perform digital filtering to allow for identification of specific components of the signal, and statistically analyze the resulting signal. These quantitative analyses have created the potential to decrease the time required for EEG reviewers, allow for seizures to be more accurately detected with a simpler metric, and prevent confusion of symptom detection, thus providing for a more effective and efficient diagnosis. Many companies have addressed this opportunity for development and designed systems, each with their own name and features. This article attempts to explain the techniques for signal transformation that are starting to see wide use and point out some of the benefits of this type of interpretation that have been identified in the literature. [ABSTRACT FROM AUTHOR]
ISSN:	1086508X
DOI:	10.1080/1086508x.2008.11079678