Investigation of EEG signals of patients with major depression using chaotic features

In this study, the EEG signals of major depression (MD) patients and healthy control subjects were investigated using different chaotic features. The acquired signals during 3 minutes were compared using complexity measures such as Katz fractal, Higuchi fractal dimension, Lempel-Ziv complexity (LZC) and Kolmogorov complexity (KC) in MATLAB between two groups. In order to determine which complexity measure is more effective in discriminating MD patients from control subjects, statistical variance analyses were performed. As a result, it was found that patients had increased EEG complexity and better discrimination were obtained using the LZC and KC.