Parkinson Hastalığının Derecesi ile Yürüyüş Değişkenliği Arasındaki İlişkinin Bulanık Tekrarlılık Grafiğine Göre Araştırılması

Parkinson hastalığı (PH) beyindeki ilerleyici nöron kaybıyla ilgili olup milyonlarca insanın hayatını olumsuz yönde etkilemektedir.PH’nin tanısı genellikle radyonüklid pozitron yayınlayıcı tomografi veya tek foton emisyonlu bilgisayarlı tomografi gibi bazı kliniktestler kullanılarak dopaminerjik nöronlardaki düşüşün belirlenmesine dayanır. Bununla beraber hastalığa uzaktan tanıkoyulabilmesine yönelik çeşitli çalışmalarda literatürde yer almaktadır. PH’yi engelleyen veya iyileştiren bir tedavi yöntemiolmamakla birlikte hastalığın çeşitli belirtilerine yönelik kısmi tedaviler uygulanmaktadır. Motor ve motor olmayan belirtiler arasındatitreme, sertlik, postüral dengesizlik, depresyon ve kaygı gibi çeşitli faktörler vardır. Bu çeşitli belirtilerle birlikte Parkinsonhastalarının yürüyüş değişkenliği gösterdikleri saptanmıştır. Bu çalışmada Parkinson hastalarının yürüyüş verileri incelenerek, PH’ninderecesi ile yürüyüş değişkenliği arasındaki ilişki ortaya konmuştur. Yürüyüş sinyalleri tek boyutlu sinyaller şeklinde olup bu verilerbulanık tekrarlılık grafiği yöntemi ile görselleştirilmiştir. Bulanık tekrarlılık grafiği ile zaman serisi şeklindeki sinyaller dokusalbilgiler içeren resme dönüştürülmüştür. Görselleştirilen verilerde gri seviyeli eş-zamanlılık matrisi kullanılarak otokorelasyon,kontrast, korelasyon, küme önceliği, küme gölgesi, benzeşmezlik, enerji, entropi, homojenlik ve maksimum olasılık parametrelerihesaplanmıştır. Hesaplanan parametrelerin PH değerleme ölçekleri olan Hoehn&Yahr, UPDRS ve MDS-UPDRS ile ilişkisiaraştırılmıştır. Elde edilen sonuçlara göre otokorelasyon, küme önceliği, enerji, entropi, ve maksimum olasılık parametreleri tümdeğerleme ölçekleri ile korele olduğu saptanmıştır. Bunlardan entropi pozitif korelasyon gösterirken, diğerleri negatif korelasyonasahiptir. Korelasyon ve küme gölgesi parametrelerinin ise üç değerleme ölçeği ile de ilişkisi olmadığı belirlenmiştir. Hoehn&Yahrdeğerleme ölçeğinin diğer ölçeklere göre genel anlamda daha yüksek sonuçlar ortaya koyması ayırt ediciliğinin daha fazla olduğunuortaya koymaktadır. Bu çalışmanın yenilikçi yanı yürüyüş değişkenliği ile PH’nin derecesi arasındaki ilişkinin hesaplamalıyöntemlerle ortaya konmasıdır.

Investigation of the Relationship between Severity of Parkinson’s Disease and Gait Variability Based on Fuzzy Recurrence Plot

Parkinson's disease is a neurodegenerative disease that negatively affects millions of lives. The diagnosis of Parkinson's disease is usually based on determining the decrease in dopaminergic neurons using some clinical tests, such as radionuclide positron emission tomography or single photon emission computed tomography. Nevertheless, there are various studies in the literature to diagnose the disease remotely. Although there is no available treatment method yet that prevents or cures Parkinson's disease, partial treatments are applied for various symptoms of the disease. Motor and non-motor symptoms include tremor, stiffness, postural instability, depression, and anxiety. Along with these various symptoms, Parkinson's patients were found to exhibit gait variability. In this study,the gait signals of Parkinson's disease patients were examined and the relationship between severity of Parkinson's disease and gait variability was revealed. Gait signals are one dimensional signals and they were visualized with fuzzy recurrence plot method. Time series signals were converted to images, which contains textural information, by the aid of fuzzy recurrence plot. In the visualized data, autocorrelation, contrast, correlation, cluster priority, cluster shadow, dissimilarity, energy, entropy, homogeneity and maximum probability parameters were computed by using gray level co-occurrence matrix. The relationship between the computed parameters and, Hoehn&Yahr, UPDRS and MDS-UPDRS, which are rating scales to assess severity of Parkinson’s disease, were evaluated. According to the obtained results autocorrelation, cluster priority, energy, entropy, and maximum probability parameters were found to be correlated with all rating scales. Although entropy shows a positive correlation, others have a negative correlation. Correlation and cluster shadow parameters were found to be not related to the rating scales. The fact that the Hoehn&Yahr rating scale has higher results, reveals that it is more discriminative. The innovative part of this study is demonstration of the relationship between gait variability and the severity of Parkinson's disease with computational methods.

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