Reliability of Electroencephalogram-Based Individual Markers – Case Study*

The aim of this study was to evaluate individual level of natural variability of electroencephalogram (EEG) based markers. Three linear: alpha power variability, spectral asymmetry index, relative gamma power and three nonlinear methods: Higuchi’s fractal dimension, detrended fluctuation analysis, and Lempel-Ziv complexity were selected. The markers were evaluated over 15 sessions acquired in 14 months. The results indicate that individual natural variability for five of the selected markers is lower compared to differences between healthy and depressed groups of subjects in our previous studies. The results of the current study suggest that EEG based markers can be applied for evaluation of disturbances in brain activity at individual level.Clinical Relevance—The indicated stability in the current study of widely used EEG-based markers at individual level suggests a promising opportunity to apply EEG as a novel method in diagnoses of brain mental disorders in clinical practice.

[1]  Steve Horvath,et al.  Resting-State Quantitative Electroencephalography Reveals Increased Neurophysiologic Connectivity in Depression , 2012, PloS one.

[2]  C. Peng,et al.  Mosaic organization of DNA nucleotides. , 1994, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[3]  Francesco Carlo Morabito,et al.  Longitudinal study of alzheimer's disease degeneration through EEG data analysis with a NeuCube spiking neural network model , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

[4]  Hiie Hinrikus,et al.  Spectral Asymmetry and Higuchi's Fractal Dimension Measures of Depression Electroencephalogram , 2013, Comput. Math. Methods Medicine.

[5]  Hiie Hinrikus,et al.  Single channel EEG analysis for detection of depression , 2017, Biomed. Signal Process. Control..

[6]  C. Dolea,et al.  World Health Organization , 1949, International Organization.

[7]  Paul Bebbington,et al.  The World Health Report 2001 , 2001, Social Psychiatry and Psychiatric Epidemiology.

[8]  H. Stanley,et al.  Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. , 1995, Chaos.

[9]  B. Oken,et al.  Test-retest reliability in EEG frequency analysis. , 1991, Electroencephalography and clinical neurophysiology.

[10]  Hiie Hinrikus,et al.  Assessment of Objective Symptoms of Depression in Occupational Health Examination. , 2019, Journal of occupational and environmental medicine.

[11]  T. Thiagarajan,et al.  EEG Frequency Bands in Psychiatric Disorders: A Review of Resting State Studies , 2019, Front. Hum. Neurosci..

[12]  Ganesh B. Janvale,et al.  Ovarian hormones and the brain signals , 2009 .

[13]  Hiie Hinrikus,et al.  EEG Spectral Asymmetry Index Reveals Effect of Microwave Radiation , 2011 .

[14]  Francesco Carlo Morabito,et al.  A Longitudinal EEG Study of Alzheimer's Disease Progression Based on A Complex Network Approach , 2015, Int. J. Neural Syst..

[15]  J. Pickens,et al.  EEG Stability in Infants/Children of Depressed Mothers , 1997, Child psychiatry and human development.

[16]  Jang-Han Lee,et al.  Detrended fluctuation analysis of resting EEG in depressed outpatients and healthy controls , 2007, Clinical Neurophysiology.

[17]  T. Higuchi Approach to an irregular time series on the basis of the fractal theory , 1988 .

[18]  Jaan Raik,et al.  Surrogate Data Method Requires End-Matched Segmentation of Electroencephalographic Signals to Estimate Non-linearity , 2018, Front. Physiol..

[19]  Hiie Hinrikus,et al.  Electroencephalographic spectral asymmetry index for detection of depression , 2009, Medical & Biological Engineering & Computing.

[20]  Nalini K. Ratha,et al.  Enhancing security and privacy in biometrics-based authentication systems , 2001, IBM Syst. J..

[21]  B.S. Raghavendra,et al.  Signal characterization using Fractal Dimension , 2008, TENCON 2008 - 2008 IEEE Region 10 Conference.

[22]  A John Rush,et al.  High Frequency EEG Activity during Sleep: Characteristics in Schizophrenia and Depression , 2005, Clinical EEG and neuroscience.

[23]  Abraham Lempel,et al.  On the Complexity of Finite Sequences , 1976, IEEE Trans. Inf. Theory.

[24]  V. Knott,et al.  EEG power, frequency, asymmetry and coherence in male depression , 2001, Psychiatry Research: Neuroimaging.

[25]  S. Tong,et al.  Abnormal EEG complexity in patients with schizophrenia and depression , 2008, Clinical Neurophysiology.

[26]  Erik W. Jensen,et al.  EEG complexity as a measure of depth of anesthesia for patients , 2001, IEEE Trans. Biomed. Eng..

[27]  Rikkert Hindriks,et al.  Thalamo-cortical mechanisms underlying changes in amplitude and frequency of human alpha oscillations , 2013, NeuroImage.

[28]  HosseinifardBehshad,et al.  Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal , 2013 .

[29]  Arno Villringer,et al.  Sex hormones affect neurotransmitters and shape the adult female brain during hormonal transition periods , 2015, Front. Neurosci..

[30]  John Read,et al.  Heterogeneity in psychiatric diagnostic classification , 2019, Psychiatry Research.

[31]  S. Leistedt,et al.  Characterization of the sleep EEG in acutely depressed men using detrended fluctuation analysis , 2007, Clinical Neurophysiology.

[32]  Carlos M Travieso,et al.  EEG biometric identification: a thorough exploration of the time-frequency domain. , 2015, Journal of neural engineering.

[33]  Hiie Hinrikus,et al.  Methods for classifying depression in single channel EEG using linear and nonlinear signal analysis , 2018, Comput. Methods Programs Biomed..

[34]  Alexander A. Fingelkurts,et al.  Composition of brain oscillations in ongoing EEG during major depression disorder , 2006, Neuroscience Research.