Nonlinear analysis of EEG complexity in episode and remission phase of recurrent depression

2018/11/11 by Milena Čukić, Čukić Milena, Miodrag Stokić +8 · 78 citations
Biochemistry, Genetics and Molecular Biology · Medicine · Neuroscience · Psychology · #Clinical psychology #Depression (economics) #EEG and Brain-Computer Interfaces #Economics #Electroencephalography #Functional Brain Connectivity Studies #Heart Rate Variability and Autonomic Control #Keynesian economics #Nonlinear system #Phase (matter) #Physics #Psychiatry #Psychology #q-bio.NC

paper · pdf · doi:10.1002/mpr.1816

published in International Journal of Methods in Psychiatric Research 29(2), e1816 (Wiley) · 23 pages, 6 figures

arxiv created 2018/11/11 · openalex publication_date 2019/12/09 · arxiv updated 2019/12/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06

Abstract

OBJECTIVES: Biomarkers of major depressive disorder (MDD), its phases and forms have long been sought. Objectives were to examine whether the complexity of EEG activity, measured by Higuchi's fractal dimension (HFD) and sample entropy (SampEn), differs between healthy subjects, patients in remission, and in episode phase of the recurrent depression and whether the changes are differentially distributed between hemispheres and cortical regions. METHODS: Resting state EEG with eyes closed was recorded from 22 patients suffering from recurrent depression (11 in remission, 11 in the episode), and 20 age and sex-matched healthy control subjects. Artifact-free EEG epochs were analyzed by in-house developed programs running HFD and SampEn algorithms. RESULTS: Depressed patients had higher HFD and SampEn complexity compared to healthy subjects. The complexity was higher in patients who were in remission than in those in the acute episode. Altered complexity was present in the frontal and centro-parietal regions when compared to control group. The complexity in frontal and parietal regions differed between the two phases of depressive disorder. CONCLUSIONS: Complexity measures of EEG distinguish between the healthy controls, patients in remission and episode. Further studies are needed to establish whether these measures carry a potential to aid clinically relevant decisions about depression.

Citations

• Physiological time-series analysis using approximate entropy and sample entropy
• CLASSIFICATION OF EEG SIGNALS IN NORMAL AND DEPRESSION CONDITIONS BY ANN USING RWE AND SIGNAL ENTROPY
• Approach to an irregular time series on the basis of the fractal theory
• Dynamical principles in neuroscience
• Frontal EEG asymmetry as a moderator and mediator of emotion
• Evaluating Online Labor Markets for Experimental Research: Amazon.com's Mechanical Turk
• Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal
• Atypical depression and non-atypical depression: Is HPA axis function a biomarker? A systematic review

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