Open AccessMultiscale ApEn and SampEn in Quantifying Nonlinear Complexity of Depressed MEG
Author(s) -
Yao Wenpo,
Hu Hui,
Wang Jun,
Yan Wei,
Li Jin,
Hou Fengzhen
Publication year - 2019
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2018.06.007
Subject(s) - sample entropy , approximate entropy , magnetoencephalography , neurophysiology , psychology , major depressive disorder , depression (economics) , nonlinear system , electroencephalography , neuroscience , mood , cognitive psychology , psychiatry , pattern recognition (psychology) , physics , quantum mechanics , economics , macroeconomics
Depression is a neurophysiological disorder with recurrent dysregulations of self‐mental states. Multiscale Approximate entropy (ApEn) and Sample entropy (SampEn) are employed to characterize nonlinear complexity of Magnetoencephalography (MEG) of depressive patients in our contribution. SampEn shares similarities with ApEn while has better distinctions between the MEGs of depression patients and normal people. Test results prove that nonlinear complexity of the depressive MEG is lower than that of the normal subjects, indicating weaker response of depression patients to emotional stimuli, and the optimum discriminations between the depressive and healthy people lie in frontal lobe of brain which is related to emotional regulation. Our. ndings provide valuable information about depression, highlight the loss of nonlinear complexity in MEG of depressive patient and can be used as clinical diagnostic aids.