A Hybrid System for Distinguishing between Brain Death and Coma Using Diverse EEG Features

Abstract

Electroencephalography (EEG) signals may provide abundant information reflecting the developmental changes in brain status. It usually takes a long time to finally judge whether a brain is dead, so an effective pre-test of brain states method is needed. In this paper, we present a hybrid processing pipeline to differentiate brain death and coma patients based on canonical correlation analysis (CCA) of power spectral density, complexity features, and feature fusion for group analysis. In addition, time-varying power spectrum and complexity were observed based on the analysis of individual patients, which can be used to monitor the change of brain status over time. Results showed three major differences between brain death and coma groups of EEG signal: slowing, increased complexity, and the improvement on classification accuracy with feature fusion. To the best of our knowledge, this is the first scheme for joint general analysis and time-varying state monitoring. Delta-band relative power spectrum density and permutation entropy could effectively be regarded as potential features of discrimination analysis on brain death and coma patients.

Molecular biogeography of planktonic and benthic diatoms in the Yangtze River

“…To investigate the difference of diatoms in the plateau and the non-plateau areas, we implemented an additional synchronous monitoring along the source river of the Yangtze located at the Qinghai-Tibet Plateau in July 2017 (the ideal month for sampling in the plateau), collected 12 water and 12 sediment samples respectively at 12 sites. Although the most ideal sampling should be the synchronous monitoring in both plateau and non-plateau areas in the same year, the above remedial sampling is helpful considering insignificant inter-annual variations of water quality, riverine habitat, and aquatic organism in the plateau in recent years [66, 67]. Except for a very few samples missed due to restrictions of steep terrain and rapid flow as described in a previous study [30], up to four parallel samples were collected in most cases.…”

Electrophysiological and Neuroimaging Studies – During Resting State and Sensory Stimulation in Disorders of Consciousness: A Review

“…Non-linear analysis of resting EEG using indices like complexity and entropy has also been used in several studies (Sarà and Pistoia, 2010;Gosseries et al, 2011;Sarà et al, 2011;Wu et al, 2011;Sitt et al, 2014;Stefan et al, 2018;Zhu et al, 2019) to quantify the degree of consciousness in DOC patients. Entropy of EEG is a measure of its regularity.…”

Using time causal quantifiers to characterize sleep stages

“…Bandt and Pompe propose the concept of permutation entropy for quantifying the complexity of a system behind a time series ( [22]). This methodology has been applied for investigating EEG in different contexts as such as epilepsy ( [23]), coma ( [24]), anesthetics ( [25]), and particularly in sleep ( [26][27][28]), showing better results to traditional analyzes and representing a robust approach to time series analysis based on counting ordinal patterns. The complexity of the brain would represent the amount of "information" contained in the organism, in the sense that it quantifies the dynamical features of the temporal patterns due to functional interactions produced by the underlying structural network.…”