A Study on the Effects of Sympathetic Skin Response Parameters in Diagnosis of Fibromyalgia Using Artificial Neural Networks

Abstract

Fibromyalgia syndrome (FMS), usually observed commonly in females over age 30, is a rheumatic disease accompanied by extensive chronic pain. In the diagnosis of the disease non-objective psychological tests and physiological tests and laboratory test results are evaluated and clinical experiences stand out. However, these tests are insufficient in differentiating FMS with similar diseases that demonstrate symptoms of extensive pain. Thus, objective tests that would help the diagnosis are needed. This study analyzes the effect of sympathetic skin response (SSR) parameters on the auxiliary tests used in FMS diagnosis, the laboratory tests and physiological tests. The study was conducted in Suleyman Demirel University, Faculty of Medicine, Physical Medicine and Rehabilitation Clinic in Turkey with 60 patients diagnosed with FMS for the first time and a control group of 30 healthy individuals. In the study all participants underwent laboratory tests (blood tests), certain physiological tests (pulsation, skin temperature, respiration) and SSR measurements. The test data and SSR parameters obtained were classified using artificial neural network (ANN). Finally, in the ANN framework, where only laboratory and physiological test results were used as input, a simulation result of 96.51 % was obtained, which demonstrated diagnostic accuracy. This data, with the addition of SSR parameter values obtained increased to 97.67 %. This result including SSR parameters - meaning a higher diagnostic accuracy - demonstrated that SSR could be a new auxillary diagnostic method that could be used in the diagnosis of FMS.