Literature DB >> 19471748

[Spectral entropy: a new method for anesthetic adequacy.].

Rogean Rodrigues Nunes1, Murilo Pereira de Almeida, James Wallace Sleigh.   

Abstract

BACKGROUND AND OBJECTIVES: Though universally employed, clinical signs to evaluate anesthetic adequacy are not reliable. Over the past years several pieces of equipment have been devised to improve intraoperative handling of anesthetic drugs, some of them directly measuring cerebral cortical activity (hypnosis). None of them, however, has offered the possibility of directly evaluating sub-cortical activity (motor response). CONTENTS: Spectral entropy measures irregularity, complexity or amount of EEG disorders and has been proposed as indicator of anesthetic depth. Signal is collected from the fronto-temporal region and processed according to Shannon's equation (H = - Sp k log p k, where p k represents the probability of a discrete k event), resulting in two types of analyses: 1) state entropy (SE), which evaluates cerebral cortex electrical activity (0.8 - 32Hz) and 2) response entropy (RE), containing both subcortical electromyographic and cortical electroence- phalographic components and analyzes frequencies in the range 0.8 - 47Hz.
CONCLUSIONS: Frontal muscles activation may indicate inadequacy of the subcortical component (nociception). Such activation appears as a gap between SE and RE. This, it is possible to directly evaluate both cortical (SE) and subcortical (RE) components providing better anesthetic adequacy.

Entities:  

Year:  2004        PMID: 19471748

Source DB:  PubMed          Journal:  Rev Bras Anestesiol        ISSN: 0034-7094            Impact factor:   0.964


  5 in total

1.  EEG signal analysis: a survey.

Authors:  D Puthankattil Subha; Paul K Joseph; Rajendra Acharya U; Choo Min Lim
Journal:  J Med Syst       Date:  2010-04       Impact factor: 4.460

2.  Alpha desynchronization during Stroop test unmasks cognitively healthy individuals with abnormal CSF Amyloid/Tau.

Authors:  Xianghong Arakaki; Shao-Min Hung; Roger Rochart; Alfred N Fonteh; Michael G Harrington
Journal:  Neurobiol Aging       Date:  2021-12-05       Impact factor: 4.673

3.  Categorisation of EEG suppression using enhanced feature extraction for SUDEP risk assessment.

Authors:  Juan C Mier; Yejin Kim; Xiaoqian Jiang; Guo-Qiang Zhang; Samden Lhatoo
Journal:  BMC Med Inform Decis Mak       Date:  2020-12-24       Impact factor: 2.796

4.  Identifying Amnestic Mild Cognitive Impairment With Convolutional Neural Network Adapted to the Spectral Entropy Heat Map of the Electroencephalogram.

Authors:  Xin Li; Yi Liu; Jiannan Kang; Yu Sun; Yonghong Xu; Yi Yuan; Ying Han; Ping Xie
Journal:  Front Hum Neurosci       Date:  2022-07-06       Impact factor: 3.473

5.  Alpha desynchronization during simple working memory unmasks pathological aging in cognitively healthy individuals.

Authors:  Xianghong Arakaki; Ryan Lee; Kevin S King; Alfred N Fonteh; Michael G Harrington
Journal:  PLoS One       Date:  2019-01-02       Impact factor: 3.240

  5 in total

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