Search

Objective: Constructing a framework to evaluate consciousness is an important issue in neuroscience research and clinical practice. However, there is still no systematic framework for quantifying altered consciousness along the dimensions of both level and content. This study builds a framework to differentiate the following states: coma, general anesthesia, minimally conscious state (MCS), and normal wakefulness. Methods: This study analyzed electroencephalography (EEG) recorded from frontal channels in patients with disorders of consciousness (either coma or MCS), patients under general anesthesia, and healthy participants in normal waking consciousness (NWC). Four non-linear methods—permutation entropy (PE), sample entropy (SampEn), permutation Lempel-Ziv complexity (PLZC), and detrended fluctuation analysis (DFA)—as well as relative power (RP), extracted features from the EEG recordings. A genetic algorithm-based support vector machine (GA-SVM) classified the states of consciousness based on the extracted features. A multivariable linear regression model then built EEG indices for level and content of consciousness. Results: The PE differentiated all four states of consciousness (p<0.001). Altered contents of consciousness for NWC, MCS, coma, and general anesthesia were best differentiated by the SampEn, and PLZC. In contrast, the levels of consciousness for these four states were best differentiated by RP of Gamma and PE. A multi-dimensional index, combined with the GA-SVM, showed that the integration of PE, PLZC, SampEn, and DFA had the highest classification accuracy (92.3%). The GA-SVM was better than random forest and neural networks at differentiating these four states. The ‘coordinate value’ in the dimensions of level and content were constructed by the multivariable linear regression model and the non-linear measures PE, PLZC, SampEn, and DFA. Conclusions: Multi-dimensional measurements, especially the PE, SampEn, PLZC, and DFA, when combined with GA-SVM, are promising methods for constructing a framework to quantify consciousness. [ABSTRACT FROM AUTHOR]

Using single and multiple dielectric barrier discharge plasma actuators, the velocity fields induced by steady, pulsed, asynchronous, and duty cycle plasma actuation are studied based on Particle Image Velocimetry (PIV). First, the flow fields induced by a single dielectric barrier discharge with different discharge models and actuation parameters are investigated. According to the velocity distributions, a parametric study on the influence of excitation voltages, carrier frequencies, pulse frequencies, and duty ratios on plasma actuation is presented and the optimum actuation parameters are provided. Emphatically, through double actuators alternating discharge, the peristaltic acceleration plasma actuation, a new plasma actuation mode, has been proposed and studied. The efficiency of the plasma actuators in steady and pulsed discharge modes is explored. The velocity field induced by plasma actuation with different phase angles and duty cycle ratios is acquired and the momentum transfer characteristics of the flow field were discussed. Finally, based on the above-mentioned results, the technical superiority and mechanism of the peristaltic acceleration induced by multiple dielectric barrier discharge plasma actuators are analyzed. [ABSTRACT FROM PUBLISHER]