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