Automated analysis and trending of the raw EEG signal.
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| 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] |
| Copyright of American Journal of Electroneurodiagnostic Technology is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: pbh DbLabel: Psychology and Behavioral Sciences Collection An: 105965853 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Automated analysis and trending of the raw EEG signal. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Anderson+NR%22">Anderson NR</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wisneski+KJ%22">Wisneski KJ</searchLink> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22American+Journal+of+Electroneurodiagnostic+Technology%22">American Journal of Electroneurodiagnostic Technology</searchLink>. Sep2008, Vol. 48 Issue 3, p166-191. 26p. – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of American Journal of Electroneurodiagnostic Technology is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=105965853 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/1086508x.2008.11079678 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 166 Titles: – TitleFull: Automated analysis and trending of the raw EEG signal. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Anderson NR – PersonEntity: Name: NameFull: Wisneski KJ IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2008 Type: published Y: 2008 Identifiers: – Type: issn-print Value: 1086508X Numbering: – Type: volume Value: 48 – Type: issue Value: 3 Titles: – TitleFull: American Journal of Electroneurodiagnostic Technology Type: main |
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