EEG machine learning with Higuchi fractal dimension and Sample Entropy as features for successful detection of depression

TL;DR

This study demonstrates that EEG features derived from Higuchi Fractal Dimension and Sample Entropy, combined with machine learning, can accurately distinguish depressed patients from healthy controls, offering a promising diagnostic tool.

Contribution

The paper introduces the use of HFD and SampEn as EEG features for depression detection, showing high classification accuracy with various machine learning algorithms.

Findings

Average accuracy ranged from 90.24% to 97.56%.

Sample Entropy outperformed Higuchi Fractal Dimension.

Effective discrimination achieved with few principal components.

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. We 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.