Abstract

We developed a method to distinguish bursts and suppressions for EEG burst suppression from the treatments of status epilepticus, employing the joint time-frequency domain. We obtained the feature used in the proposed method from the joint use of the time and frequency domains, and we estimated the decision as to whether the measured EEG was a burst segment or suppression segment by the maximum likelihood estimation. We evaluated the performance of the proposed method in terms of its accordance with the visual scores and estimation of the burst suppression ratio. The accuracy was higher than the sole use of the time or frequency domains, as well as conventional methods conducted in the time domain. In addition, probabilistic modeling provided a more simplified optimization than conventional methods. Burst suppression quantification necessitated precise burst suppression segmentation with an easy optimization; therefore, the excellent discrimination and the easy optimization of burst suppression by the proposed method appear to be beneficial.

Highlights

  • Electroencephalogram (EEG) burst suppression represents an inactivated EEG pattern, in which the aperiodic alternation of an isoelectric pattern and a high voltage pattern appears

  • In the case of a burst suppression caused by anesthesia, the duration of the burst or suppression varies depending on the level of anesthetic concentration, with high levels identifying their relevance to a long duration of suppression [6, 8, 9]

  • Four patients whose EEG clearly showed a burst suppression pattern were selected for the quantitative EEG (qEEG) analysis in this study, using artifact-free EEG segments for at least 20 minutes chosen by an expert neurologist

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Summary

Introduction

Electroencephalogram (EEG) burst suppression represents an inactivated EEG pattern, in which the aperiodic alternation of an isoelectric pattern (suppression) and a high voltage pattern (bursts) appears. The methods of burst suppression segmentation developed far mainly involve detecting burst events by using certain features, such as Shannon entropy [19, 20], a nonlinear energy operator [21], line length [14], a voltage envelope [22], and variance using recursive-variance estimation [16] These all represent features employed in the time domain, as well as occasionally the basic features of the frequency domain, like 3 or 10 Hz power or mean power spectral density (PSD) [19, 20, 23].

Methods
Conventional methods
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