Independent Component Analysis Removing Artifacts in Ictal Recordings

Abstract

Summary:Purpose: Independent component analysis (ICA) is a novel algorithm able to separate independent components from complex signals. Studies in interictal EEG demonstrate its usefulness to eliminate eye, muscle, 50-Hz, electrocardiogram (ECG), and electrode artifacts. The goal of this study was to evaluate the usefulness of ICA in removing artifacts in ictal recordings with a known EEG onset.Methods: We studied 20 seizures of nine patients with focal epilepsy monitored in our video-EEG monitoring unit. ICA was applied to remove obvious artifacts in segments at the beginning of the seizure. The final EEGs were exported to the original format and were compared with the original EEG by two blinded examiners. We compared original recordings and the samples cleaned by digital filters (DFs), ICA and ICA plus digital filters (ICA + DFs), evaluating the possibility of finding an ictal pattern, the localization of the onset in area and time, and the global quality of the sample.Results: All the recordings except one (95%) improved after the use of ICA for the elimination of blinking and other artifacts. Three seizures were found in which in the original recordings did not permit us to detect an ictal pattern, and after ICA + DFs, an ictal onset was evident; in two of them, ICA alone was able to show this pattern. The best results in all the scores were obtained with ICA + DF. ICA was better than DFs. The agreement between the two reviewers was highly significant.Conclusions: ICA is useful to remove artifacts from ictal recordings. When applied to ictal recordings, it increases the quality of the recording. In some cases, ICA may be useful to show ictal onsets obscured by artifacts. ICA + DFs obtained the best results regarding removal of the artifacts.

Analysis of the dynamics and origin of epileptic activity in patients with tuberous sclerosis evaluated for surgery of epilepsy

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Muscle Artifact Removal from Human Sleep EEG by Using Independent Component Analysis

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“…We focused on temporalis because together with frontalis muscles represent the most common sources of muscle artifacts during resting EEG conditions. 2,37 The four selected ICA algorithms have been previously proposed as valid methods for EEG artifact correction, 30,32,33,35,48,49,52,56 but they differ in both their theoretical assumptions and the underlying statistic fundamentals. Two of them (AMUSE and SOBI) are based on second-order statistics (SOS) and can separate independent sources by minimizing correlations between signals.…”