title: Automated analysis and trending of the raw EEG signal.
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 signal features, perform digitalfiltering 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.
authors.affiliation: Department of Biomedical Engineering Washington University in St. Louis St. Louis, Missouri, USA.
authors.forename: Nicholas R
authors.initials: NR
authors.lastname: Anderson
authors.forename: Kimberly J
authors.initials: KJ
authors.lastname: Wisneski
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003627:Data Interpretation, Statistical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted