Cha Kab-Mun, Choi Byung-Moon, Noh Gyu-Jeong, Shin Hyun-Chool
Department of Electronic Engineering, Soongsil University, Seoul, Republic of Korea.
Department of Clinical Pharmacology and Therapeutics, Asan Medical Center, Seoul, Republic of Korea.
Comput Intell Neurosci. 2017;2017:3521261. doi: 10.1155/2017/3521261. Epub 2017 Mar 16.
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.
在本文中，我们提出了通过量化多通道脑电图中的主要信息流来测量麻醉深度（DOA）的新方法。传统方法主要独立使用少数脑电图通道，并且大多数基于多通道脑电图的研究仅限于大脑的特定区域。因此，在麻醉深度测量中很难反映大脑广泛区域的大脑皮层功能。在此，通过对从主二分法获得的主要信息流进行量化来测量麻醉深度。使用三种二分法来检测整个脑电图通道中的主要信息流，并通过计算信息熵对主要信息流进行量化。从39名受试者的临床数据实验结果证实了所提出的测量方法与丙泊酚血浆浓度之间的高度相关性。为了更轻松地说明所提出方法的性能，我们在二维（2D）脑图上展示了多通道脑电图的结果。
