publication . Other literature type . Article . Preprint . 2020

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

Miodrag Stokić;
  • Published: 25 Mar 2020
  • Publisher: Springer Science and Business Media LLC
Abstract
Reliable diagnosis of depressive disorder is essential for both optimal treatment and prevention of fatal outcomes. In this study, we aimed to elucidate the effectiveness of two non-linear measures, Higuchi 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 Naive Bayes classifier, discriminating EEG between healthy control subjects and patients diagnosed with depression...
Subjects
Medical Subject Headings: food and beverages
free text keywords: Cognitive Neuroscience, Statistics - Machine Learning, Computer Science - Machine Learning, Quantitative Biology - Neurons and Cognition
Powered by OpenAIRE Open Research Graph
Any information missing or wrong?Report an Issue
publication . Other literature type . Article . Preprint . 2020

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

Miodrag Stokić;