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Computational and Mathematical Methods in Medicine
Volume 2016 (2016), Article ID 2684731, 12 pages
Research Article

Novel Burst Suppression Segmentation in the Joint Time-Frequency Domain for EEG in Treatment of Status Epilepticus

1Department of Electrical Engineering, POSTECH, Pohang, Gyeongbuk, Republic of Korea
2Department of Neurology, Ewha Womans University School of Medicine and Ewha Medical Research Institute, Seoul, Republic of Korea
3Department of Electronic Engineering, Soongsil University, Seoul, Republic of Korea

Received 9 June 2016; Revised 10 September 2016; Accepted 5 October 2016

Academic Editor: Valeri Makarov

Copyright © 2016 Jaeyun Lee et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.