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

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 Naı ¨ve 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.

Construction of embedded fMRI resting-state functional connectivity networks using manifold learning

“…At this point we should note that our intention was not to try to obtain the best possible classification performance by ''optimising'' the pre-processing of the raw fMRI data and/ or by trying to find the best set of graph-theoretic measures [other studies have already shed light towards this direction (Č ukić et al 2020;Xiang et al 2020;Vergara et al 2017)]. For example Č ukić et al (2020) showed that successful discrimination of depression from EEG could be attributed to proper feature extraction and not to a particular classification method.…”

Is EEG a Useful Examination Tool for Diagnosis of Epilepsy and Comorbid Psychiatric Disorders?

“…In many studies on the diagnosis of depression and schizophrenia [88][89][90][91][92][93][94][95][96][97], the nonlinear EEG analysis have been reported, but no nonlinear EEG analysis with accuracy has been reported for comorbid psychotic disorders and depression in patients with epilepsy. Psychiatric comorbidities are common in patients with epilepsy [3], and associations for psychosis with the age at onset, duration of epilepsy, and seizure frequency have been reported.…”

Prediction And Detection In Change Of Cognitive Load For VIP's By A Machine Learning Approach

“…Milena Cukic [77] worked out that for effective therapy and to avoid tragic results, a reliable diagnosis of depressive illness is required. The main focus of this study was the performance of Higuchi's Fractal Dimension and Sample Entrop on EEG in diagnosing psychological illnesses.…”