Novel Methods for Measuring Depth of Anesthesia by Quantifying Dominant Information Flow in Multichannel EEGs

المؤلفون المشاركون

Noh, Gyu-Jeong
Shin, Hyun-Chool
Cha, Kab-Mun
Choi, Byung-Moon

المصدر

Computational Intelligence and Neuroscience

العدد

المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-14، 14ص.

الناشر

Hindawi Publishing Corporation

تاريخ النشر

2017-03-16

دولة النشر

مصر

عدد الصفحات

14

التخصصات الرئيسية

الأحياء

الملخص EN

In this paper, we propose novel methods for measuring depth of anesthesia (DOA) by quantifying dominant information flow in multichannel EEGs.

Conventional methods mainly use few EEG channels independently and most of multichannel EEG based studies are limited to specific regions of the brain.

Therefore the function of the cerebral cortex over wide brain regions is hardly reflected in DOA measurement.

Here, DOA is measured by the quantification of dominant information flow obtained from principle bipartition.

Three bipartitioning methods are used to detect the dominant information flow in entire EEG channels and the dominant information flow is quantified by calculating information entropy.

High correlation between the proposed measures and the plasma concentration of propofol is confirmed from the experimental results of clinical data in 39 subjects.

To illustrate the performance of the proposed methods more easily we present the results for multichannel EEG on a two-dimensional (2D) brain map.

نمط استشهاد جمعية علماء النفس الأمريكية (APA)

Cha, Kab-Mun& Choi, Byung-Moon& Noh, Gyu-Jeong& Shin, Hyun-Chool. 2017. Novel Methods for Measuring Depth of Anesthesia by Quantifying Dominant Information Flow in Multichannel EEGs. Computational Intelligence and Neuroscience،Vol. 2017, no. 2017, pp.1-14.
https://search.emarefa.net/detail/BIM-1140907

نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)

Cha, Kab-Mun…[et al.]. Novel Methods for Measuring Depth of Anesthesia by Quantifying Dominant Information Flow in Multichannel EEGs. Computational Intelligence and Neuroscience No. 2017 (2017), pp.1-14.
https://search.emarefa.net/detail/BIM-1140907

نمط استشهاد الجمعية الطبية الأمريكية (AMA)

Cha, Kab-Mun& Choi, Byung-Moon& Noh, Gyu-Jeong& Shin, Hyun-Chool. Novel Methods for Measuring Depth of Anesthesia by Quantifying Dominant Information Flow in Multichannel EEGs. Computational Intelligence and Neuroscience. 2017. Vol. 2017, no. 2017, pp.1-14.
https://search.emarefa.net/detail/BIM-1140907

نوع البيانات

مقالات

لغة النص

الإنجليزية

الملاحظات

Includes bibliographical references

رقم السجل

BIM-1140907