A Study on the Effects of Sympathetic Skin Response Parameters in Diagnosis of Fibromyalgia Using Artificial Neural Networks.
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 st...
| Publicado en: | Journal of Medical Systems Vol. 40; no. 3; pp. 1 - 10 |
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| Autores principales: | , , , , , , , |
| Formato: | pictorial research tables/charts tracings Journal Article |
| Publicado: |
Springer Nature
Mar2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=115925275&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 115925275 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Mar2016 vid: 40 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 115925275 115925275 115925275 10.1007/s10916-015-0406-0 115925275 ppf: 1 ppct: 9 formats: fmt: @attributes: type: P tig: atl: A Study on the Effects of Sympathetic Skin Response Parameters in Diagnosis of Fibromyalgia Using Artificial Neural Networks. aug: au: Ozkan, Ozhan Yildiz, Murat Arslan, Evren Yildiz, Sedat Bilgin, Suleyman Akkus, Selami Koyuncuoglu, Hasan Koklukaya, Etem affil: Department of Electrical and Electronics Engineering, Faculty of Engineering, Sakarya University, 54187 Sakarya Turkey sug: subj: Fibromyalgia Diagnosis Autonomic Nervous System Skin Physiology Neural Networks (Computer) Utilization Signal Processing, Computer Assisted Methods Human Academic Medical Centers Descriptive Statistics Data Analysis Software Discriminant Analysis T-Tests Turkiye Funding Source ab: 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. pubtype: Academic Journal doctype: pictorial research tables/charts tracings Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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