IJIGSP Vol. 8, No. 7, 8 Jul. 2016
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Biomedical signal processing, Fibromyalgia syndrome, Beck depression inventory, Artificial neural networks, Sympathetic Skin Response
Fibromyalgia is a chronic pain syndrome that generally appears with prevalent muscular pain, sleep disorder and fatigue. Its diagnosis is very difficult. It is diagnosed in a long time after evaluating variety of psychological test scores along with physiological and laboratory tests. Psychological tests are thought not to be as reliable as laboratory test results since they depend on oral reports of the patients, and can differ from patient to patient. Beck depression inventory is one of the psychological test scores. In this study, a new biological signal that could be used instead of Beck depression inventory is introduced. For this purpose, sympathetic skin responses were used along with physiological and laboratory test results that were collected both from diagnosed fibromyalgia patients and healthy patients. A relationship based on classification was aimed to be established between the data and Beck depression inventory by using artificial neural networks. Three different artificial neural network training algorithm were used in the study. According to the results, physiological and laboratory test results and back depression inventory were estimated with the accuracy rate of 77.70\%. When all the data were used with Levenberg-Marquardt back propagation training algorithm, this rate went up to 90.91\%. According to these results, sympathetic skin responses can be safely used instead of Beck depression inventory when they were used along with other parameters that were used in fibromyalgia diagnosis.
Muhammed Kürşad Uçar, Mehmet Recep Bozkurt, Ferda Bozkurt,"Sympathetic Skin Response: A New Biological Signal that can be used in Diagnosis of Fibromyalgia Instead of Beck Depression Inventory", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.8, No.7, pp.32-40, 2016. DOI: 10.5815/ijigsp.2016.07.04
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