Parkinson Hastalığının Teşhisinde YSA Destekli Karar Sistemi Başarımı

Year 2020, Ejosat Special Issue 2020 (ARACONF), 8 - 14, 01.04.2020

Ugur Fidan , Neşe Özkan

https://doi.org/10.31590/ejosat.araconf2

Abstract

Parkinson hastalığı (PD), dopamin üreten beyin hücrelerinin işlev kaybıyla sonuçlanan nörodejeneratif bir hastalıktır. PD'nin primer tanımı; hastaların% 70'inde üst ve alt ekstremitelerde titreme,% 30'unda harekette yavaşlama ve sertlik gibi görülür. Arşimet spiral tekniği, PD motor bozukluklarını incelemek için geliştirilmiş bir klinik test yöntemidir. Spiral test çizim tekniğinin güvenilirliği ve geçerliliği, Birleşik Tahmin Derecelendirme Ölçeği (UPDRS) ile karşılaştırılarak istatistiksel olarak kanıtlanmıştır. Bu çalışmada, statik bir spiral test ve dinamik bir spiral test çizimlerinin yapılması, sinyal işleme teknikleri kullanılarak karakteristiklerin çıkarılması ve yapay sinir ağı modeli kullanılarak Parkinson hastalığının belirlenmesi amaçlanmıştır. Hastalığın sınıflandırılmasında, sadece sınıflandırmada sadece DST ve f skor oranı kullanan SST ve YSA sırasıyla 0.95 ve 0.92 olarak bulundu. SST ve DST yöntemleri birlikte değerlendirildiğinde YSA sınıflandırma başarısı 0,99 bulunmuştur. Bu nedenle, hastalığın sınıflandırılmasında SST ve DST yöntemlerinin sadece SST ve DST kullanan sınıflandırmalardan daha başarılı olduğu bulunmuştur. Çalışma sonucunda SST ve DST verilerinin kombinasyonu kullanılarak PD,% 98.6 doğruluk ve 0.99 f skoru ile yapay zeka teknikleri ile sınıflandırılmıştır.

Keywords

Biyomedikal Sinyal İşleme, Parkinson Hastalığı, Yapay Sinir Ağları (YSA), Spiral Analiz

ANN Supported Decision System Performance in Diagnosing Parkinson’s Disease

Abstract

Parkinson’s disease (PD) is a neurodegenerative disease that results in the loss of function of dopamine- producing brain cells. Primer designation of PD; is seen as tremor in the upper and lower limbs in 70% of the patients, and as in slowing and stiffness in the movement in 30% of them. Archimedes spiral technique is a clinical test method developed for examining PD motor disorders. The reliability and validity of the spiral test drawing technique was statistically proven by comparing it with the Unified Predictive Rating Scale (UPDRS). In this study, it was aimed to construct a static spiral test and a dynamic spiral test drawings, to extract the characteristics using the signal processing techniques and to identify the Parkinson’s disease using the artificial neural network model. In the classification of the disease, only SST and ANN using only DST and f score ratio in the classification were found to be 0.95 and 0.92, respectively. When SST and DST methods were evaluated together, ANN classification success was found to be 0.99. For this reason, it was found that SST and DST methods were more successful in the classification of the disease than the classification using SST and DST alone. Using the combination of SST and DST data as a result of the study, PD was classified with artificial intelligence techniques with an accuracy of 98.6% and a score of 0.99 f.

Keywords

Biomedical Signal Analysis, Parkinson’s Disease, Artificial Neural Network (ANN), Spiral Analysis

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