Xiong Xin, Feng Jiannan, Zhang Yaru, Wu Di, Yi Sanli, Wang Chunwu, Liu Ruixiang, He Jianfeng
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China.
College of Physics and Electronic Engineering, Hanshan Normal University, Chaozhou, China.
Front Neurosci. 2023 May 24;17:1174399. doi: 10.3389/fnins.2023.1174399. eCollection 2023.
Substance addiction is a chronic disease which causes great harm to modern society and individuals. At present, many studies have applied EEG analysis methods to the substance addiction detection and treatment. As a tool to describe the spatio-temporal dynamic characteristics of large-scale electrophysiological data, EEG microstate analysis has been widely used, which is an effective method to study the relationship between EEG electrodynamics and cognition or disease.
To study the difference of EEG microstate parameters of nicotine addicts at each frequency band, we combine an improved Hilbert Huang Transformation (HHT) decomposition with microstate analysis, which is applied to the EEG of nicotine addicts.
After using improved HHT-Microstate method, we notice that there is significant difference in EEG microstates of nicotine addicts between viewing smoke pictures group (smoke) and viewing neutral pictures group (neutral). Firstly, there is a significant difference in EEG microstates at full-frequency band between smoke and neutral group. Compared with the FIR-Microstate method, the similarity index of microstate topographic maps at alpha and beta bands had significant differences between smoke and neutral group. Secondly, we find significant class × group interactions for microstate parameters at delta, alpha and beta bands. Finally, the microstate parameters at delta, alpha and beta bands obtained by the improved HHT-microstate analysis method are selected as features for classification and detection under the Gaussian kernel support vector machine. The highest accuracy is 92% sensitivity is 94% and specificity is 91%, which can more effectively detect and identify addiction diseases than FIR-Microstate and FIR-Riemann methods.
Thus, the improved HHT-Microstate analysis method can effectively identify substance addiction diseases and provide new ideas and insights for the brain research of nicotine addiction.
物质成瘾是一种对现代社会和个人造成极大危害的慢性疾病。目前，许多研究已将脑电图（EEG）分析方法应用于物质成瘾的检测和治疗。作为描述大规模电生理数据时空动态特征的工具，EEG微状态分析已被广泛应用，它是研究EEG电动力学与认知或疾病之间关系的有效方法。
为研究尼古丁成瘾者各频段EEG微状态参数的差异，我们将改进的希尔伯特-黄变换（HHT）分解与微状态分析相结合，并应用于尼古丁成瘾者的EEG。
使用改进的HHT-微状态方法后，我们发现尼古丁成瘾者观看吸烟图片组（吸烟）和观看中性图片组（中性）的EEG微状态存在显著差异。首先，吸烟组和中性组在全频段的EEG微状态存在显著差异。与FIR-微状态方法相比，α和β频段微状态地形图的相似性指数在吸烟组和中性组之间存在显著差异。其次，我们发现δ、α和β频段的微状态参数存在显著的类别×组交互作用。最后，将改进的HHT-微状态分析方法得到的δ、α和β频段微状态参数作为高斯核支持向量机下分类和检测的特征。最高准确率为92%，灵敏度为94%，特异性为91%，比FIR-微状态和FIR-黎曼方法能更有效地检测和识别成瘾疾病。
因此，改进的HHT-微状态分析方法能有效识别物质成瘾疾病，为尼古丁成瘾的脑研究提供新的思路和见解。
