Literature DB >> 20595085

An improved detrended moving-average method for monitoring the depth of anesthesia.

T Nguyen-Ky1, Peng Wen, Yan Li.   

Abstract

The detrended moving-average (DMA) method is a new approach to quantify correlation properties in nonstationary signals with underlying trends. This paper monitored the depth of anesthesia (DoA) using modified DMA (MDMA) method. MDMA provides a power-law relation between the fluctuation function F(MDMA)(s) and the scale s: F(MDMA)(s)αs(α), where α is the slope of F(MDMA)(s) in the logarithm scale. We applied the MDMA to monitor the DoA by computing the scaling exponent F(α) and F(min) values. To validate the proposed method, we compared our results with the bispectral index (BIS) monitor. We found a close correlation between our results and BIS with r(F (min)) = 0.9346, r(2)(F(min)) = 0.9183, and r(F(α)) = 0.9458, r(2)(F(α)) = 0.8855. Our method reflects the state of consciousness of a patient undergoing general anesthesia faster than BIS as observed clinically. The minimum time delay between the BIS and F(min) trends was 12 s and the maximum was 178 s. Furthermore, in the case of poor signal quality, our results agreed with clinical observation, which indicates that our method can accurately estimate a patient's hypnotic state in such circumstances. F(α) and F(min) trends are responsive and their movement seems similar to changes in the clinical state of the patients.

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Year:  2010        PMID: 20595085     DOI: 10.1109/TBME.2010.2053929

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  8 in total

1.  Long-range temporal correlations in the brain distinguish conscious wakefulness from induced unconsciousness.

Authors:  Thomas Thiery; Tarek Lajnef; Etienne Combrisson; Arthur Dehgan; Pierre Rainville; George A Mashour; Stefanie Blain-Moraes; Karim Jerbi
Journal:  Neuroimage       Date:  2018-06-07       Impact factor: 6.556

2.  Multi-scale sample entropy of electroencephalography during sevoflurane anesthesia.

Authors:  Yinghua Wang; Zhenhu Liang; Logan J Voss; Jamie W Sleigh; Xiaoli Li
Journal:  J Clin Monit Comput       Date:  2014-01-11       Impact factor: 2.502

3.  Frontal-temporal functional connectivity of EEG signal by standardized permutation mutual information during anesthesia.

Authors:  Fahimeh Afshani; Ahmad Shalbaf; Reza Shalbaf; Jamie Sleigh
Journal:  Cogn Neurodyn       Date:  2019-08-22       Impact factor: 5.082

4.  A novel spectral entropy-based index for assessing the depth of anaesthesia.

Authors:  Jee Sook Ra; Tianning Li; Yan Li
Journal:  Brain Inform       Date:  2021-05-12

5.  EEG entropy measures in anesthesia.

Authors:  Zhenhu Liang; Yinghua Wang; Xue Sun; Duan Li; Logan J Voss; Jamie W Sleigh; Satoshi Hagihira; Xiaoli Li
Journal:  Front Comput Neurosci       Date:  2015-02-18       Impact factor: 2.380

6.  Quantifying the depth of anesthesia based on brain activity signal modeling.

Authors:  Hyub Huh; Sang-Hyun Park; Joon Ho Yu; Jisu Hong; Mee Ju Lee; Jang Eun Cho; Choon Hak Lim; Hye Won Lee; Jun Beom Kim; Kyung-Sook Yang; Seung Zhoo Yoon
Journal:  Medicine (Baltimore)       Date:  2020-01       Impact factor: 1.889

7.  Assessment of Anesthesia Depth Using Effective Brain Connectivity Based on Transfer Entropy on EEG Signal.

Authors:  Neda Sanjari; Ahmad Shalbaf; Reza Shalbaf; Jamie Sleigh
Journal:  Basic Clin Neurosci       Date:  2021-03-01

8.  Real-Time Depth of Anaesthesia Assessment Based on Hybrid Statistical Features of EEG.

Authors:  Yi Huang; Peng Wen; Bo Song; Yan Li
Journal:  Sensors (Basel)       Date:  2022-08-15       Impact factor: 3.847

  8 in total

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