Study on the Correlation of Microstate Sequences of MEG

Conference: BIBE 2019 - The Third International Conference on Biological Information and Biomedical Engineering
06/20/2019 - 06/22/2019 at Hangzhou, China

Proceedings: BIBE 2019

Pages: 5Language: englishTyp: PDF

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Zhang, Yafei; Wang, Jun (Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province,Nanjing University of Posts and Telecommunications, Nanjing, China)
Yan, Wei (Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China)
Li, Jin (College of Physics and Information Technology, Shaanxi Normal University, Xi’an, China)
Hou, Fengzhen (School of Science, China Pharmaceutical University, Nanjing, China)

Magnetoencephalography(MEG) as a non-inva-sive brain function detection technology has been widely used in the study of depression. We analyze the Hurst exponent to study the correlation of the microstate sequences of MEG between patients with depression and healthy people. We implement four microstate segmentation algorithms, namely modified K-Means, principal component analysis (PCA), fast independent component analysis (Fast-ICA) and K-medoids, to cluster MEG into microstate sequences, and then calculate Hurst exponent through R/S and DFA method. The experimental results show that the correlation of MEG between healthy people and depressed patients has a significant difference, and we also find that the correlation of MEG of depressed patients in the left and right frontal regions of the brain is different distinctly from that of healthy people, which may provide a judgment basis for diagnosis and assessment of the risk of depression.