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Cognitive Neurodynamics

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25.03.2020 | Research Article

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

verfasst von: Milena Čukić, Miodrag Stokić, Slobodan Simić, Dragoljub Pokrajac

Erschienen in: Cognitive Neurodynamics | Ausgabe 4/2020

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Abstract

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.

Vorheriger Artikel Bacomics: a comprehensive cross area originating in the studies of various brain–apparatus conversations

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Titel: The successful discrimination of depression from EEG could be attributed to proper feature extraction and not to a particular classification method
verfasst von: Milena Čukić
Miodrag Stokić
Slobodan Simić
Dragoljub Pokrajac
Publikationsdatum: 25.03.2020
Verlag: Springer Netherlands
Erschienen in: Cognitive Neurodynamics / Ausgabe 4/2020
Print ISSN: 1871-4080
Elektronische ISSN: 1871-4099
DOI: https://doi.org/10.1007/s11571-020-09581-x

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