Year 2020, Volume , Issue 19, Pages 410 - 419 2020-08-31

Parkinson Hastalığının Derecesi ile Yürüyüş Değişkenliği Arasındaki İlişkinin Bulanık Tekrarlılık Grafiğine Göre Araştırılması
Investigation of the Relationship between Severity of Parkinson’s Disease and Gait Variability Based on Fuzzy Recurrence Plot

İsmail CANTÜRK [1]


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 klinik testler kullanılarak dopaminerjik nöronlardaki düşüşün belirlenmesine dayanır. Bununla beraber hastalığa uzaktan tanı koyulabilmesine yönelik çeşitli çalışmalarda literatürde yer almaktadır. PH’yi engelleyen veya iyileştiren bir tedavi yöntemi olmamakla birlikte hastalığın çeşitli belirtilerine yönelik kısmi tedaviler uygulanmaktadır. Motor ve motor olmayan belirtiler arasında titreme, sertlik, postüral dengesizlik, depresyon ve kaygı gibi çeşitli faktörler vardır. Bu çeşitli belirtilerle birlikte Parkinson hastalarının yürüyüş değişkenliği gösterdikleri saptanmıştır. Bu çalışmada Parkinson hastalarının yürüyüş verileri incelenerek, PH’nin derecesi ile yürüyüş değişkenliği arasındaki ilişki ortaya konmuştur. Yürüyüş sinyalleri tek boyutlu sinyaller şeklinde olup bu veriler bulanık tekrarlılık grafiği yöntemi ile görselleştirilmiştir. Bulanık tekrarlılık grafiği ile zaman serisi şeklindeki sinyaller dokusal bilgiler 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 parametreleri hesaplanmıştır. Hesaplanan parametrelerin PH değerleme ölçekleri olan Hoehn&Yahr, UPDRS ve MDS-UPDRS ile ilişkisi araştırılmıştır. Elde edilen sonuçlara göre otokorelasyon, küme önceliği, enerji, entropi, ve maksimum olasılık parametreleri tüm değerleme ölçekleri ile korele olduğu saptanmıştır. Bunlardan entropi pozitif korelasyon gösterirken, diğerleri negatif korelasyona sahiptir. Korelasyon ve küme gölgesi parametrelerinin ise üç değerleme ölçeği ile de ilişkisi olmadığı belirlenmiştir. Hoehn&Yahr değerleme ölçeğinin diğer ölçeklere göre genel anlamda daha yüksek sonuçlar ortaya koyması ayırt ediciliğinin daha fazla olduğunu ortaya koymaktadır. Bu çalışmanın yenilikçi yanı yürüyüş değişkenliği ile PH’nin derecesi arasındaki ilişkinin hesaplamalı yöntemlerle ortaya konmasıdır.

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. 

  • Abdulhay, E., Arunkumar, N., Narasimhan, K., Vellaiappan, E., & Venkatraman, V. (2018). Gait and tremor investigation using machine learning techniques for the diagnosis of Parkinson disease. Future Generation Computer Systems, 83, 366-373.
  • Afonso, L. C., Rosa, G. H., Pereira, C. R., Weber, S. A., Hook, C., Albuquerque, V. H. C., & Papa, J. P. (2019). A recurrence plot-based approach for Parkinson’s disease identification. Future Generation Computer Systems, 94, 282-292.
  • AYDIN, F., & Aslan, Z. (2017). Yapay öğrenme yöntemleri ve dalgacık dönüşümü kullanılarak nöro dejeneratif hastalıkların teşhisi. Journal of the Faculty of Engineering and Architecture of Gazi University, 32(3), 749-766.
  • Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences, 10(2-3), 191-203.
  • Cantürk, İ., & Karabiber, F. (2016). A Machine Learning System for the Diagnosis of Parkinson’s Disease from Speech Signals and Its Application to Multiple Speech Signal Types. Arabian Journal for Science and Engineering, 41(12), 5049-5059. doi:10.1007/s13369-016-2206-3
  • Chaudhuri, K. R., & Schapira, A. H. (2009). Non-motor symptoms of Parkinson's disease: dopaminergic pathophysiology and treatment. The Lancet Neurology, 8(5), 464-474.
  • Chok, N. S. (2010). Pearson's versus Spearman's and Kendall's correlation coefficients for continuous data. University of Pittsburgh,
  • Conditions, N. C. C. f. C. (2006). Parkinson's disease: national clinical guideline for diagnosis and management in primary and secondary care.
  • De Lau, L., Giesbergen, P., De Rijk, M., Hofman, A., Koudstaal, P., & Breteler, M. (2004). Incidence of parkinsonism and Parkinson disease in a general population: the Rotterdam Study. Neurology, 63(7), 1240-1244.
  • Disease, M. D. S. T. F. o. R. S. f. P. s. (2003). The unified Parkinson's disease rating scale (UPDRS): status and recommendations. Movement Disorders, 18(7), 738-750.
  • Eckmann, J., Kamphorst, S. O., & Ruelle, D. (1995). Recurrence plots of dynamical systems. World Scientific Series on Nonlinear Science Series A, 16, 441-446.
  • Fahn, S., & Elton, R. (1987). UPDRS program members. Unified Parkinsons disease rating scale. Recent developments in Parkinson’s disease, 2, 153-163.
  • Frenkel-Toledo, S., Giladi, N., Peretz, C., Herman, T., Gruendlinger, L., & Hausdorff, J. M. (2005). Effect of gait speed on gait rhythmicity in Parkinson's disease: variability of stride time and swing time respond differently. Journal of neuroengineering and rehabilitation, 2(1), 23.
  • Frenkel‐Toledo, S., Giladi, N., Peretz, C., Herman, T., Gruendlinger, L., & Hausdorff, J. M. (2005). Treadmill walking as an external pacemaker to improve gait rhythm and stability in Parkinson's disease. Movement disorders: official journal of the Movement Disorder Society, 20(9), 1109-1114.
  • Goetz, C. G., Fahn, S., Martinez‐Martin, P., Poewe, W., Sampaio, C., Stebbins, G. T., . . . Dubois, B. (2007). Movement Disorder Society‐sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS‐UPDRS): process, format, and clinimetric testing plan. Movement Disorders, 22(1), 41-47.
  • Goetz, C. G., Poewe, W., Rascol, O., Sampaio, C., Stebbins, G. T., Counsell, C., . . . Wenning, G. K. (2004). Movement Disorder Society Task Force report on the Hoehn and Yahr staging scale: status and recommendations the Movement Disorder Society Task Force on rating scales for Parkinson's disease. Movement Disorders, 19(9), 1020-1028.
  • Goetz, C. G., Tilley, B. C., Shaftman, S. R., Stebbins, G. T., Fahn, S., Martinez‐Martin, P., . . . Dodel, R. (2008). Movement Disorder Society‐sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS‐UPDRS): scale presentation and clinimetric testing results. Movement disorders: official journal of the Movement Disorder Society, 23(15), 2129-2170.
  • Goldberger AL, A. L., Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. (2003). PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation, 101(23), e215-e220.
  • Gündüz, H. Parkinson Hastalığı Tespitinde Farklı Boyutsallık İndirgeme Yöntemlerinin Karşılaştırılması. Avrupa Bilim ve Teknoloji Dergisi(17), 1164-1172.
  • Haralick, R. M., & Shanmugam, K. (1973). Textural features for image classification. IEEE Transactions on systems, man, and cybernetics(6), 610-621.
  • Hausdorff, J. M., Lowenthal, J., Herman, T., Gruendlinger, L., Peretz, C., & Giladi, N. (2007). Rhythmic auditory stimulation modulates gait variability in Parkinson's disease. European journal of neuroscience, 26(8), 2369-2375.
  • Hoehn, M. M., & Yahr, M. D. (1967). Parkinsonism: onset, progression, and mortality. Neurology, 17(5), 427-427.
  • Italian, N. S. (2003). Treatment of Parkinson's disease. Neurological sciences: official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology, 24, S165.
  • Khatamino, P., Cantürk, İ., & Özyılmaz, L. (2018). A Deep Learning-CNN Based System for Medical Diagnosis: An Application on Parkinson’s Disease Handwriting Drawings. Paper presented at the 2018 6th International Conference on Control Engineering & Information Technology (CEIT).
  • Naseer, A., Rani, M., Naz, S., Razzak, M. I., Imran, M., & Xu, G. (2019). Refining Parkinson’s neurological disorder identification through deep transfer learning. Neural Computing and Applications, 1-16.
  • Pham, T. (2017). Fuzzy recurrence plots. EPL (Europhysics Letters), 116(5), 50008.
  • Pham, T. D. (2017). Texture classification and visualization of time series of gait dynamics in patients with neuro-degenerative diseases. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26(1), 188-196.
  • Pham, T. D., & Yan, H. (2017). Tensor Decomposition of Gait Dynamics in Parkinson's Disease. Ieee Transactions on Biomedical Engineering, 65(8), 1820-1827.
  • Poewe, W., Seppi, K., Tanner, C. M., Halliday, G. M., Brundin, P., Volkmann, J., . . . Lang, A. E. (2017). Parkinson disease. Nature reviews Disease primers, 3, 17013.
  • Rao, S. S., Hofmann, L. A., & Shakil, A. (2006). Parkinson’s disease: diagnosis and treatment. Am Fam Physician, 74(12), 2046-2054.
  • Sakar, B. E., Isenkul, M. E., Sakar, C. O., Sertbas, A., Gurgen, F., Delil, S., . . . Kursun, O. (2013). Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings. Ieee Journal of Biomedical and Health Informatics, 17(4), 828-834.
  • Soh, L.-K., & Tsatsoulis, C. (1999). Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices. IEEE Transactions on geoscience and remote sensing, 37(2), 780-795.
  • Tsanas, A., Little, M. A., McSharry, P. E., & Ramig, L. O. (2010). Accurate telemonitoring of Parkinson's disease progression by noninvasive speech tests. Ieee Transactions on Biomedical Engineering, 57(4), 884-893.
  • van der Heeden, J. F., Marinus, J., Martinez-Martin, P., Rodriguez-Blazquez, C., Geraedts, V. J., & van Hilten, J. J. (2016). Postural instability and gait are associated with severity and prognosis of Parkinson disease. Neurology, 86(24), 2243-2250.
  • Yogev, G., Giladi, N., Peretz, C., Springer, S., Simon, E. S., & Hausdorff, J. M. (2005). Dual tasking, gait rhythmicity, and Parkinson's disease: which aspects of gait are attention demanding? European journal of neuroscience, 22(5), 1248-1256.
  • Yücelbaş, C., & Yücelbaş, Ş. (2019). Çift Yoğunluklu 1-D Dalgacık Dönüşümü Kullanılarak Parkinson Hastalığının Yaş Faktörüne Göre Tespit Edilmesi. Avrupa Bilim ve Teknoloji Dergisi(17), 881-887.
  • Yücelbaş, Ş., & Yücelbaş, C. (2019). Temel Bileşen Analizi Yöntemleri Kullanarak Parkinson Hastalığının Otomatik Teşhisi. Avrupa Bilim ve Teknoloji Dergisi(16), 294-300.
Primary Language tr
Subjects Engineering
Journal Section Articles
Authors

Orcid: 0000-0003-0690-1873
Author: İsmail CANTÜRK (Primary Author)
Institution: YILDIZ TEKNİK ÜNİVERSİTESİ
Country: Turkey


Dates

Publication Date : August 31, 2020

APA Cantürk, İ . (2020). Parkinson Hastalığının Derecesi ile Yürüyüş Değişkenliği Arasındaki İlişkinin Bulanık Tekrarlılık Grafiğine Göre Araştırılması . Avrupa Bilim ve Teknoloji Dergisi , (19) , 410-419 . DOI: 10.31590/ejosat.699099