Parkinson Hastalığının Teşhisinde YSA Destekli Karar Sistemi Başarımı

Öz

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.

Anahtar Kelimeler

• 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

References

1. Massano, J., & Bhatia, K. P., “Clinical approach to Parkinson’s disease: features, diagnosis, and principles of management”, Cold Spring Harbor perspectives in medicine, 2(6), a008870, 2012.
2. Weiner, W. J., Shulman, L. M. and Lang, A. E., “Parkinson’s disease: A complete guide for patients and families”, The Johns Hopkins University Pres, Baltimore, 2006.
3. N. Singh, V. Pillay, and Y. E. Choonara., “Advances in the treatment of Parkinson’s disease”, Prog. Neurobiol.,vol. 81, no. 1, pp. 29–44, 2007.
4. Aygül, R., & Demir, R., “ Parkinson’s Disease Diagnostic Criteria”, Türkiye Klinikleri Journal of Neurology Special Topics, 5(4), 53-57, 2012.
5. Apaydın, H., & Özekmekci, S., “Parkinson’s Disease: Handbook for Patients and Families”, The Parkinson’s Disease Association, İstanbul, 2008.
6. Pullman, S. L., “Spiral analysis: a new technique for measuring tremor with a digitizing tablet”, Movement Disorders, 13(S3), 85-89, 1998.
7. Van Gemmert, A. W. A., Adler, C. H., & Stelmach, G. E., “Parkinson’s disease patients undershoot target size in handwriting and similar tasks”, Journal of Neurology, Neurosurgery & Psychiatry, 74(11), 1502-1508, 2003.
8. Saunders-Pullman, R., Derby, C., Stanley, K., Floyd, A., Bressman, S., Lipton, R. B. & Pullman, S. L., “Validity of spiral analysis in early Parkinson’s disease”, Movement disorders, 23(4), 531-537, 2008.

9. Sakar, B. E., Isenkul, M. E., Sakar, C. O., Sertbas, A., Gurgen, F., Delil, S., ... & Kursun, O., “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, 2013.
10. Isenkul, M., Sakar, B., & Kursun, O., “Improved spiral test using digitized graphics tablet for monitoring Parkinson’s disease”, In Proc. of the Int. Conf. on e-Health and Telemedicine (pp. 171-175), 2014.
11. San Luciano, M., Wang, C., Ortega, R. A., Yu, Q., Boschung, S., Soto-Valencia, J. & Saunders-Pullman, R., “Digitized spiral drawing: A possible biomarker for early Parkinson’s disease”, PloS one, 11(10), e0162799, 2016.
12. Zham, P., Kumar, D. K., Dabnichki, P., Poosapadi Arjunan, S., & Raghav, S.,” Distinguishing different stages of Parkinson’s disease using composite index of speed and pen-pressure of sketching a spiral”, Frontiers in Neurology, 8, 435, 2017.
13. UCIParkinson.https : // archive.ics.uci.edu / ml / datasets / Parkinson + Disease + Spiral + Drawings + Using + Digitized + Graphics + Tablet. Release date September 9, 2009. Access date December 12, 2017.
14. Kayran, A., Ek¸sioglu, E.M., Digital Signal Processing with Computer Applications, Birsen Publishing House, 1st Edition, İstanbul, Türkiye, 2004.
15. Öztemel, E., Artificial neural networks. Papatya Publishing, 2003.
16. Özkan,Ö .,Yıldız.M.,Köklükaya, E., “Enhancement of Diagnostic Accuracy Supported by Sympathetic Skin Response Parameters of Laboratory Tests Used in the Diagnosis of Fibromyalgia Syndrome”, SAÜ, Science Journal,15(1),1-7, 2011.
17. Sağıroğlu, S¸., Beşdok, E., Erler, M., Artificial Intelligence in Engineering-I, Ufuk Kitap Kırtasiye-Yayıncılık Tic Ltd. 2003.
18. The MathWorks, Inc., MATLAB Documentation Neural Network Toolbox Help, “Levenberg-Marquardt Algorithm”, Release 2009a, 2009.
19. Liu, M., Lu, X., & Song, J., “A new feature selection method for text categorization of customer reviews”, Communications in Statistics-Simulation and Computation, 45(4), 1397-1409, 2016.
20. Hausdorff, J. M., Lertratanakul, A., Cudkowicz, M. E., Peterson, A. L., Kaliton, D., & Goldberger, A. L.,”Dynamic markers of altered gait rhythm in amyotrophic lateral sclerosis”, Journal of applied physiology, 88(6), 2045-2053,2000.
21. Sakar, C. O., & Kursun, O., “Telediagnosis of Parkinson’s disease using measurements of dysphonia”, Journal of medical systems, 34(4), 591-599, 2010.
22. Sakar, B. E., Serbes, G., & Sakar, C. O., “Analyzing the effectiveness of vocal features in early telediagnosis of Parkinson’s disease”, PloS one, 12(8), e0182428, 2017.
23. Sakar, C. O., Serbes, G., Gunduz, A., Tunc, H. C., Nizam, H., Sakar, B. E., ... & Apaydin, H., “A comparative analysis of speech signal processing algorithms for Parkinson’s disease classification and the use of the tunable Q- factor wavelet transform”, Applied Soft Computing, 74, 255-263,2019.
24. Lee, S. H., & Lim, J. S., ”Parkinson’s disease classification using gait characteristics and wavelet-based feature extraction”, Expert Systems with Applications, 39(8), 7338-7344,2012.
25. Bilgin, S., “The impact of feature extraction for the classification of amyotrophic lateral sclerosis among neurodegenerative diseases and healthy subjects”, Biomedical Signal Processing and Control, 31, 288-294, 2017.
26. Zeng, W., Liu, F., Wang, Q., Wang, Y., Ma, L., & Zhang, Y., “Parkinson’s disease classification using gait analysis via deterministic learning”, Neuroscience letters, 633, 268-278, 2016.
27. Baratin, E., Sugavaneswaran, L., Umapathy, K., Ioana, C., & Krishnan, S., “Wavelet-based characterization of gait signal for neurological abnormalities”, Gait & posture, 41(2), 634-639, 2015.