Clinical
Babak Mohammadzadeh; Mehdi Khodabandelu; Masoud Lotfizadeh
Volume 3, Issue 3 , September 2016, , Pages 246-258
Abstract
Background and aims: 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 ...
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Background and aims: 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.Methods: 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.Results: 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%.Conclusion: 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.
Neurology
Babak Mohammadzadeh; Mehdi Khodabandelu; Masoud Lotfizadeh
Volume 3, Issue 1 , February 2016, , Pages 42-52
Abstract
Background and aims: Paper-pencil tests have always its own problems in the mental disorders evaluation, including learning questions, bad or good blazon are the problems with this methodology. This study aimed to propose a new alternative method of measuring mental disorders without paper-pencil test ...
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Background and aims: Paper-pencil tests have always its own problems in the mental disorders evaluation, including learning questions, bad or good blazon are the problems with this methodology. This study aimed to propose a new alternative method of measuring mental disorders without paper-pencil test using EEG. Methods: The research society involved depressed patients referred the psychiatrist clinics in Tabriz. 107 patients were selected as samples using a convenient sampling method. The Beck test was conducted. The EEG was recorded from the F4 point concurrent with displaying the film of 5 animated emotional images from Normed Images database (IAPS). The specialized screen of this recording was designed by the author in the Biograph Infinity software of device. Other software was written by the author in order to separate the αpeak frequency average associated with any image of the recorded EEG. Then the research variablesα1peak , α2peak , α3peak , α4peak , α5peak of each patient were analyzed with SPSS. After all, another 26 patients were selected to measure the Golden Standard, sensitivity, Positive predictability, Negative predictability and ROC. Results: The results of the multiple regression analysis showed that α1peak associated with αpeakfrequancy of image 1 had more explanatory power with a beta value of 0.289 compared with other variables. Then α3peak had a high explanatory power. The regression equation for the predicting the score based on his/her EEG was found in terms of αpeak frequency.Discussion: This research showed that Beck's depression score was predictable without using any questionnaire but according to EEG with a high sensitivity (100%), specificity (30.8%), PPV (59.1%), NPPV (100%), and ROC (57.4%).