Comparing diagnosis of depression in depressed patients by EEG, based on two algorithms: Artificial Nerve Networks and Neuro-Fuzy Networks

Depression disorder is one of the most common diseases, but the diagnosis is widely complicated and controversial because of interventions, overlapping and confusing nature of the disease. So, keeping previous patients’ profile seems effective for diagnosis and treatment of present patients. Use of this memory is latent in synthetic neuro-fuzzy algorithm. Present article introduces two neuro-fuzzy and artificial neural network algorithms as an aid for psychologists and psychiatrists to diagnose and treat depression.
Neuro-fuzzy has been carried out using data evaluated by psychiatrists and scholars in Tabriz city with the convenience sampling method. Sixty-five patients were studied from whom 40 patients were taught feed forward, back propagation by artificial neural network algorithm and 14 patients were tested. An inductive neuro-fuzzy intervention created neuro-fuzzy rules to decide about depression diagnosis.
The proposed neuro-fuzzy model created better classifications. Reaching maximum accuracy of 13.97%is appropriate in diagnosis prediction. The results of the present study indicated that neuro-fuzzy is more powerful than artificial neural network with accuracy 76.88%.
Findings of the research showed the depression scores of beck inventory can be predicted and explained with the accuracy of 87% using EEG in F4 and alpha peak frequency. It can be said that such accuracy in predicting can’t be obtained by any regression or route analysis method. The research can be the first step to predict and even identify depression using taking the data directly from the brain. So, there is no need for inventory and even a specialist diagnosis.