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RISS 인기검색어
- 검색키워드
- 저자 : Joo Pangyu (검색결과 2 건)
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A robust complexity measure for noisy EEG time series under dynamic transitions during anesthesia
Joo Pangyu,Kim Seunghwan,Noh Gyu-Jeong,Choi Byung-Moon 한국물리학회 2022 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.80 No.1
Measuring complexity from noisy time series provides a crucial insight into the understanding and monitoring of pattern dynamics of complex dynamical systems, including physiological systems such as complex electroencephalogram (EEG) times series observed during anesthesia. We introduce a simple, yet noble complexity measure, called the lumped permutation entropy (LPE) based on the permutation entropy (PE), which allows a tie rank on the pattern formation and shows the robustness under the influence of strong noise, overcoming some limitations of PE and its variants in noisy signals. The robustness of LPE is demonstrated for complex time series from a typical chaotic dynamical system and is applied to empirical electroencephalographic (EEG) data obtained from subjects anesthetized by propofol. In particular, we found that the entropic complexity of EEG time series based on LPE is inversely correlated with plasma concentration of propofol and shows better performance and more robustness than PE and other types of entropic algorithms in indicating the anesthetic depth during the progress of general anesthesia. LPE can be used as complexity measure for real-time monitoring of anesthetic depth during anesthesia.
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Suppressed neural complexity during ketamine- and propofol-induced unconsciousness
Wang, Jisung,Noh, Gyu-Jeong,Choi, Byung-Moon,Ku, Seung-Woo,Joo, Pangyu,Jung, Woo-Sung,Kim, Seunghwan,Lee, Heonsoo Elsevier 2017 Neuroscience Letters Vol.653 No.-
<P><B>Abstract</B></P> <P>Ketamine and propofol have distinctively different molecular mechanisms of action and neurophysiological features, although both induce loss of consciousness. Therefore, identifying a common feature of ketamine- and propofol-induced unconsciousness would provide insight into the underlying mechanism of losing consciousness. In this study we search for a common feature by applying the concept of type-II complexity, and argue that neural complexity is essential for a brain to maintain consciousness. To test this hypothesis, we show that complexity is suppressed during loss of consciousness induced by ketamine or propofol. We analyzed the randomness (type-I complexity) and complexity (type-II complexity) of electroencephalogram (EEG) signals before and after bolus injection of ketamine or propofol. For the analysis, we use Mean Information Gain (MIG) and Fluctuation Complexity (FC), which are information-theory-based measures that quantify disorder and complexity of dynamics respectively. Both ketamine and propofol reduced the complexity of the EEG signal, but ketamine increased the randomness of the signal and propofol decreased it. The finding supports our claim and suggests EEG complexity as a candidate for a consciousness indicator.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A reduction of complexity may be a common feature of unconscious state. </LI> <LI> Complexity-randomness analysis was performed on the electroencephalogram. </LI> <LI> Ketamine (propofol) increased (decreased) the randomness of neural dynamics. </LI> <LI> Complexity decreased in both ketamine- and propofol-induced unconsciousness. </LI> </UL> </P>
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