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Detection Of Depression And Scaling Of Severity Using Six Channel EEG Data

Shalini Mahato, N. Goyal, D. Ram, Sanchita Paul
Published 2020 · Computer Science, Mathematics, Medicine

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Depression is a psychiatric problem which affects the growth of a person, like how a person thinks, feels and behaves. The major reason behind wrong diagnosis of depression is absence of any laboratory test for detection as well as severity scaling of depression. Any degradation in the working of the brain can be identified through change in the electroencephalogram (EEG) signal. Thus detection as well as severity scaling of depression is done in this study using EEG signal. In this study, features are extracted from the temporal region of the brain using six (FT7, FT8, T7, T8, TP7, TP8) channels. The linear features used are delta, theta, alpha, beta, gamma1 and gamma2 band power and their corresponding asymmetry as well as paired asymmetry. The non-linear features used are Sample Entropy (SampEn) and Detrended Fluctuation Analysis (DFA). The classifiers used are: Bagging along with three different kernel functions (Polynomial, Gaussian and Sigmoidal) of Support Vector Machine (SVM). Feature selection technique used is ReliefF. Highest classification accuracy of 96.02% and 79.19% was achieved for detection and severity scaling of depression using SVM (Gaussian Kernel Function) and ReliefF as feature selection. From the analysis, it was found that depression affects the temporal region of the brain (temporo-parietal region).It was also found that depression affects the higher frequency band features more and it affects each hemisphere differently. It can also be analysed that out of all the kernel of SVM, Gaussian kernel is more efficient to other kernels. Of all the features, combination of all paired asymmetry and asymmetry showed high classification accuracy (accuracy of 90.26% for detection of depression and accuracy of 75.31% for severity scaling).
This paper references
M. Hamilton (1960)
Depression: Causes and Treatment
A. Beck (1967)
AT Beck (1967)
Effect of Epoch Length on Power Spectrum Analysis of the EEG
W. Levy (1987)
Approximate entropy as a measure of system complexity.
Steven M. Pincus (1991)
Estimating Attributes: Analysis and Extensions of RELIEF
I. Kononenko (1994)
American Psychiatric Association (1994)
Diagnostic and statistical manual of mental disorders, 4th edn
Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series.
C. Peng (1995)
Removing electroencephalographic artifacts by blind source separation.
T. Jung (2000)
Physiological time-series analysis using approximate entropy and sample entropy.
J. Richman (2000)
Data Mining: Concepts and Techniques
Jiawei Han (2000)
Removing electroencephalog r aph i c a r t e f a c t s by b l i nd sou r c e s epa r a t i on
TP Jung (2000)
The five percent electrode system for high-resolution EEG and ERP measurements
R. Oostenveld (2001)
Impact of depressed mood on neuropsychological status in temporal lobe epilepsy
S. Paradiso (2001)
R Oostenveld (2001)
Support-Vector Networks
Corinna Cortes (2004)
EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis
A. Delorme (2004)
Theoretical and Empirical Analysis of ReliefF and RReliefF
M. Robnik-Sikonja (2004)
Alpha-band characteristics in EEG spectrum indicate reliability of frontal brain asymmetry measures in diagnosis of depression.
A. J. Niemiec (2005)
AJ Niemiec (2005)
Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis
A. Delorme (2007)
Mood disorder and epilepsy: a neurobiologic perspective of their relationship
A. Kanner (2008)
Rating Scales for Depression
C. Cusin (2009)
Applied Machine Learning
M. Richter (2009)
S Theodoridis (2009)
Rating scales for depression. In: Handbook of clinical rating scales and assessment in psychiatry and mental health, current clinical psychiatry, Baer L, Blais M a (Eds), Boston, USA, pp 7–37
C Cusin (2009)
Chapter 5 – The brain
B. Baars (2010)
Handbook of Clinical Rating Scales and Assessment in Psychiatry and Mental Health
L. Baer (2010)
Chapter 5– The brain. In: Cognition, brain, and consciousness
BJ Baars (2010)
Diagnostic and Statistical Manual of Mental Disorders
V. Mittal (2011)
Classifying depression patients and normal subjects using machine learning techniques
Behshad Hosseinifard (2011)
B Hosseinifard (2011)
GJ Tortora (2012)
Principles of anatomy and physiology
GJ Tortora (2012)
RJ Sternberg (2012)
Diagnostic and Statistical Manual of Mental Disorders
Janet B. W. Williams (2013)
Brain-Computer Interfacing for Assistive Robotics: Electroencephalograms, Recurrent Quantum Neural Networks, and User-Centric Graphical Interfaces
Vaibhav Gandhi (2014)
Analysis of EEG Signals Using Wavelet Entropy and Approximate Entropy: A Case Study on Depression Patients
S. Puthankattil (2014)
Data Mining and Analysis: Fundamental Concepts and Algorithms
Mohammed J. Zaki (2014)
SD Puthankattil (2014)
Data mining EEG signals in depression for their diagnostic value
M. Mohammadi (2015)
Analysis of EEG Signals using Nonlinear Dynamics and Chaos: A review
P. Garc (2015)
Data mining EEG
M Mohammadi (2015)
Linear vs. quadratic discriminant analysis classifier: a tutorial
A. Tharwat (2016)
Diagnostic and Statistical Manual of Mental Disorders, 4th Edition
M. Adler (2016)
A Tharwat (2016)
A wavelet-based technique to predict treatment outcome for Major Depressive Disorder
W. Mumtaz (2017)
Major Depression Detection from EEG Signals Using Kernel Eigen-Filter-Bank Common Spatial Patterns
S. Liao (2017)
Attenuation of high-frequency (30–200 Hz) thalamocortical EEG rhythms as correlate of anaesthetic action: evidence from dexmedetomidine
G. Plourde (2017)
Cognitive Psychology
Philipp Koehn (2017)
Alpha Wavelet Power as a Biomarker of Antidepressant Treatment Response in Bipolar Depression.
W. Jernajczyk (2017)
World Health Organization (2017)
W Mumtaz (2017)
Depression and other common mental disorders global health estimates. WHO Document Production Services
A Pervasive Approach to EEG-Based Depression Detection
Hanshu Cai (2018)
H Cai (2018)
Classification of Depression Patients and Normal Subjects Based on Electroencephalogram (EEG) Signal Using Alpha Power and Theta Asymmetry
Shalini Mahato (2019)
Detection of major depressive disorder using linear and non-linear features from EEG signals
Shalini Mahato (2019)
Electroencephalogram (EEG) Signal Analysis for Diagnosis of Major Depressive Disorder (MDD): A Review
Shalini Mahato (2019)
S Mahato (2019)
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