  Depression is a public health issue which severely affects one's well being
and cause negative social and economic effect for society. To rise awareness of
these problems, this publication aims to determine if long lasting effects of
depression can be determined from electoencephalographic (EEG) signals. The
article contains accuracy comparison for SVM, LDA, NB, kNN and D3 binary
classifiers which were trained using linear (relative band powers, APV, SASI)
and non-linear (HFD, LZC, DFA) EEG features. The age and gender matched dataset
consisted of 10 healthy subjects and 10 subjects with depression diagnosis at
some point in their lifetime. Several of the proposed feature selection and
classifier combinations reached accuracy of 90% where all models where
evaluated using 10-fold cross validation and averaged over 100 repetitions with
random sample permutations.
