Discrimination of Depression Levels Using Machine Learning Methods on EEG Signals

Depression is a mental disorder which has direct effects on electroencephalography (EEG) of patients, that made EEG analysis a beneficial way for a depression diagnosis. A precise system which can diagnose the depression levels based on the EEG signal would be useful support. This paper presents a m...

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Vydáno v:Iranian Conference on Electrical Engineering s. 1765 - 1769
Hlavní autoři: Mohammadi, Yousef, Hajian, Mojtaba, Moradi, Mohammad Hassan
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.04.2019
Témata:
Complexity theory
Depression
EEG
Electroencephalography
Entropy
Feature extraction
Fractal Dimensions
Fractals
Fuzzy Entropy
Fuzzy Function
Nonlinear Systems
Support vector machines
Time series analysis
ISSN:2642-9527
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Shrnutí:Depression is a mental disorder which has direct effects on electroencephalography (EEG) of patients, that made EEG analysis a beneficial way for a depression diagnosis. A precise system which can diagnose the depression levels based on the EEG signal would be useful support. This paper presents a machine learning approach to discriminate the depressed subjects to four different levels of depression, according to the Beck depression inventory (BDI-II) scores, besides the separability of different levels is investigated. In this way, we also proposed a fuzzy function based on neural network (FFNN) classifier. Our dataset contains EEG signals recorded from 60 depressed subjects with different levels of depression, under resting state, and EEG analysis was done using nonlinear features including fuzzy entropy (FuzzyEn), Katz fractal dimension (KFD) and fuzzy fractal dimension (FFD). The results indicate that KFD has a better capability in the prediction of the depression level. The proposed fuzzy classifier has demonstrated significant supremacy compared to support vector machine (SVM) in almost all experiments.
ISSN:2642-9527
DOI:10.1109/IranianCEE.2019.8786540