The successful discrimination of depression from EEG could be attributed to proper feature extraction and not to a particular classification method

Reliable diagnosis of depressive disorder is essential for both optimal treatment and prevention of fatal outcomes. This study aimed to elucidate the effectiveness of two non-linear measures, Higuchi’s Fractal Dimension (HFD) and Sample Entropy (SampEn), in detecting depressive disorders when applie...

Full description

Saved in:
Bibliographic Details
Published in:Cognitive neurodynamics 2020-08, Vol.14 (4), p.443-455
Main Authors: Čukić, Milena, Stokić, Miodrag, Simić, Slobodan, Pokrajac, Dragoljub
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial Intelligence
Biochemistry
Biomarkers
Biomedical and Life Sciences
Biomedicine
Brain
Classification
Classifiers
Clinical medicine
Cognitive Psychology
Computer Science
Data mining
Decision trees
Diagnosis
EEG
Electroencephalography
Feature extraction
Fourier transforms
Fractal geometry
Fractals
Kernel functions
Learning algorithms
Machine learning
Mental depression
Mental disorders
Methods
Multilayer perceptrons
Neurosciences
Patients
Polynomials
Research Article
Support vector machines
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Reliable diagnosis of depressive disorder is essential for both optimal treatment and prevention of fatal outcomes. This study aimed to elucidate the effectiveness of two non-linear measures, Higuchi’s Fractal Dimension (HFD) and Sample Entropy (SampEn), in detecting depressive disorders when applied on EEG. HFD and SampEn of EEG signals were used as features for seven machine learning algorithms including Multilayer Perceptron, Logistic Regression, Support Vector Machines with the linear and polynomial kernel, Decision Tree, Random Forest, and Naïve Bayes classifier, discriminating EEG between healthy control subjects and patients diagnosed with depression. This study confirmed earlier observations that both non-linear measures can discriminate EEG signals of patients from healthy control subjects. The results suggest that good classification is possible even with a small number of principal components. Average accuracy among classifiers ranged from 90.24 to 97.56%. Among the two measures, SampEn had better performance. Using HFD and SampEn and a variety of machine learning techniques we can accurately discriminate patients diagnosed with depression vs controls which can serve as a highly sensitive, clinically relevant marker for the diagnosis of depressive disorders.
ISSN:1871-4080
1871-4099
DOI:10.1007/s11571-020-09581-x