Artifact-related epilepsy

Neurology (Impact Factor: 8.29). 12/2012; 80(Issue 1, Supplement 1):S12-S25. DOI: 10.1212/WNL.0b013e3182797325


Potentials that do not conform to an expected electrical field generated by the brain characterize an extracerebral source or artifact. Artifact is present in virtually every EEG. It is an essential component for routine visual analysis, yet it may beguile the interpreter into falsely identifying waveforms that simulate epileptiform discharges (ED). The principal importance of artifact is represented by the frequency of its occurrence in contrast to the limited frequency of normal variants that may imitate pathologic ED. Continuous EEG monitoring has uncovered newly identified artifacts unique to prolonged recording. The combined use of video and EEG has revolutionized our ability to distinguish cerebral and extracerebral influences through behavioral correlation that is time-locked to the electrophysiologic features that are present on EEG. Guidelines exist to ensure minimal standards of recording. Precise definitions are present for ED. Still, the ability to distinguish artifact from pathologic ED requires a human element that is to provide the essential identification of an abnormal EEG. The ramification of a misinterpreted record carries an acute risk of treatment and long-term consequences for diagnosis-related harm. Neurology (R) 2013;80 (Suppl 1):S12-S25

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Available from: William O Tatum, Sep 03, 2015
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    ABSTRACT: Aim of the study: A novel method for removal of artifacts from long-term EEGs was developed and evaluated. The method targets most types of artifacts and works without user interaction. Materials and methods: The method is based on a neurophysiological model and utilizes an iterative Bayesian estimation scheme. The performance was evaluated by two independent reviewers. From 48 consecutive epilepsy patients, 102 twenty-second seizure onset EEGs were used to evaluate artifacts before and after artifact removal and regarding the erroneous attenuation of true EEG patterns. Results: The two reviewers found "major improvements" in 59% and 49% of the EEG epochs respectively, and "minor improvements" in 38% and 47% of the epochs, respectively. The answer "similar or worse" was chosen only in 0% and 4%, respectively. Neither of the reviewers found "major attenuations", i.e., a significant attenuation of significant EEG patterns. Most EEG epochs were found to be either "mostly preserved" or "all preserved". A "minor attenuation" was found only in 0% and 17%, respectively. Conclusions: The proposed artifact removal algorithm effectively removes artifacts from EEGs and improves the readability of EEGs impaired by artifacts. Only in rare cases did the algorithm slightly attenuate EEG patterns, but the clear visibility of significant patterns was preserved in all cases of this study. Current artifact removal methods work either semi-automatically or with insufficient reliability for clinical use, whereas the "PureEEG" method works fully automatically and leaves true EEG patterns unchanged with a high reliability.
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