Yıl: 2017 Cilt: 17 Sayı: 2 Sayfa Aralığı: 3311 - 3318 Metin Dili: İngilizce İndeks Tarihi: 29-07-2022

DIAGNOSIS OF THE PARKINSON DISEASE BY USING DEEP NEURAL NETWORK CLASSIFIER

Öz:
Parkinson disease occurs when certain clusters of brain cells are unable to generate dopamine which is needed to regulate the number of the motor and non-motor activity of the human body. Besides, contributing to speech, visual, movement, urinary problems, Parkinson disease also increases the risks of depression, anxiety, and panic attacks, disturbances of sleep. Parkinson disease diagnosis via proper interpretation of the vocal and speech data is an important classification problem. In this paper, a Parkinson disease diagnosis is realized by using the speech impairments, which is one of the earliest indicator for Parkinson disease. For this purpose, a deep neural network classifier, which contains a stacked autoencoder and a softmax classifier, is proposed. The several simulations are performed over two databases to demonstrate the effectiveness of the deep neural network classifier. The results of the proposed classifier are compared with the results of the state-of-art classification method. The experimental results and statistical analyses are showed that the deep neural network classifier is very efficient classifier for Parkinson disease diagnosis.
Anahtar Kelime:

Konular: Mühendislik, Elektrik ve Elektronik
Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
  • [1] D. G. Standaert, M. H. Saint-Hilaire, C. A. Thomas "Parkinson's Disease Handbook", American Parkinson Disease Association, New York, USA, 2015.
  • [2] J. Jankovic, "Parkinson's disease: clinical features and diagnosis", Journal of Neurology, Neurosurgery & Psychiatry, vol. 79, no. 4, pp. 368-376, 2008.
  • [3] D. J. Gelb, E. Oliver, S. Gilman, "Diagnostic criteria for Parkinson disease", Archives of Neurology, vol. 56, no. 1, pp. 33-39, 1999.
  • [4] N. Singh, V. Pillay, Y. E. Choonara, "Advances in the treatment of Parkinson's disease", Progr. Neurobiol., vol. 81, pp. 29-44, 2007.
  • [5] M. A. Little, P. E. McSharry, E. J. Hunter, J. Spielman, L. O. Ramig, "Suitability of Dysphonia Measurements for Telemonitoring of Parkinson's Disease", IEEE Transactions on Biomedical Engineering, vol. 56, no. 4, pp. 1015-1022, April 2009.
  • [6] A. K. Ho, R. Iansek, C.Marigliani, J. L. Bradshaw, S. Gates, "Speech impairment in a large sample of patients with Parkinson's disease", Behav. Neurol., vol. 11, pp. 131-137, 1998.
  • [7] J. A. Logemann, H. B. Fisher, B. Boshes, E. R. Blonsky, "Frequency and co-occurrence of vocal-tract dysfunctions in speech of a large sample of Parkinson patients", J. Speech. Hear. Disord., vol. 43, pp. 47-57, 1978.
  • [8] J. R. Duffy, "Motor Speech Disorders: Substrates, Differential Diagnosis, and Management", Elsevier eBook, 2013.
  • [9] B. E. Sakar, M. E. Isenkul, C. O. Sakar, A. Sertbas, F. Gurgen, S. Delil, H. Apaydin, O. Kursun, "Collection and Analysis of a Parkinson Speech Dataset with Multiple Types of Sound Recordings", IEEE Journal of Biomedical and Health Informatics, vol. 17, no. 4, pp. 828-834, July 2013.
  • [10] A. Tsanas, M. A. Little, P. E. McSharry, L. O. Ramig, "Accurate Telemonitoring of Parkinson's Disease Progression by Noninvasive Speech Tests", IEEE Transactions on Biomedical Engineering, vol. 57, no. 4, pp. 884-893, April 2010.
  • [11] A. Tsanas, M. A. Little, P. E. McSharry, J. Spielman and L. O. Ramig, "Novel Speech Signal Processing Algorithms for High-Accuracy Classification of Parkinson's Disease", IEEE Transactions on Biomedical Engineering, vol. 59, no. 5, pp. 1264-1271, May 2012.
  • [12] Y. LeCun, Y. Bengio, G. Hinton, "Deep Learning", Nature, vol. 521, pp. 436-444, 2015.
  • [13] A. Caliskan, H. Badem, A. Basturk, M. E. Yuksel, "Classification and Diagnosis of Cardiac Arrhythmia Disease by Deep Learning", International Conference on Artificial Intelligence and Data Processing (IDAP16), Malatya, Turkey, 2016, pp. 291-293.
  • [14] H. Badem, A. Caliskan, A. Basturk, M. E. Yuksel, "Classification and Diagnosis of the Parkinson Disease by Stacked Autoencoder", 10th International Conference on Electrical and Electronics Engineering, Bursa, Turkey, 2016, pp. 499-502.
  • [15] H. Badem, A. Caliskan, A. Basturk, M. E. Yuksel, "Classification of Human Activity by Using a Stacked Autoencoder ", Medical Technologies National Conference (TIPTEKNO'16), Antalya, Turkey, 2016, pp.370-273.
  • [16] J. Xu, L. Xiang, Q. Liu, H. Gilmore, J. Wu, J. Tang, A. Madabhushi, "Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images", IEEE Transactions on Medical Imaging, vol. 35, no. 1, pp. 119-130, Jan. 2016.
  • [17] J. Xu, X. Luo, G. Wang, H. Gilmore, A. Madabhushi, "A deep convolutional neural network for segmenting and classifying epithelial and stromal regions in histopathological images", Neurocomputing, vol. 191, pp. 214-223, 2016.
  • [18] D. T. Grozdi, S. T. Jovii, M. Suboti, "Whispered speech recognition using deep denoising autoencoder", Engineering Applications of Artificial Intelligence, vol. 59, pp. 15 - 22, 2017.
  • [19] Lichman, M. UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science, 2013.
  • [20] M. A. Little, P. E. McSharry, S. J. Roberts, D. Costello, I. M. Moroz, "Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection", Biomed. Eng., vol. 6, no. 23, 2007.
  • [21] Y. Bengio, "Practical recommendations for gradientbased trainin of deep architectures", Neural Networks: Tricks of the Trade", Springer, pp. pp. 437-478, 2012.
  • [22] M. Ranzato, C. Poultney, S. Chopra, Y. LeCun, "Efficient Learning of Sparse Representations with an EnergyBased Model", Proceedings of Neural Information and Processing Systems, 2006.
  • [23] A. Ng, "Sparse autoencoder", CS294A Lecture Notes, 2011.
  • [24] Q. Le, J. Ngiam, A. Coates, A. Lahiri, B. Prochnow, A. Ng, "On optimization methods for deep learning", Proceedings of the 28th International Conference on Machine Learning (ICML-11), 2011, pp. 265-272.
  • [25] Y. Zhang, E. Zhang, W. Chen, "Deep neural network for halftone image classification based on sparse autoencoder", Engineering Applications of Artificial Intelligence, vol. 50, pp. 245-255, 2016
  • [26] D. C. Liu, J. Nocedal, "On the limited memory BFGS method for large scale optimization. Mathematical programming, vol. 45 no.1,pp. 503-528. 1989.
  • [27] Das R., "A comparison of multiple classification methods for diagnosis of Parkinson disease", Expert Systems with Applications, vol. 37, no 2, pp.1568-1572, 2010.
  • [28] Woloszynski T., Kurzynski M., "A probabilistic model of classifier competence for dynamic ensemble selection", Pattern Recognition, vol. 44, no.10-11, pp.2656-2668, 2011.
  • [29] Sakar, C. O., Kursun O.. "Telediagnosis of Parkinson's disease using measurements of dysphonia." Journal of medical systems, vol.34, no.4, pp. 591-599, 2010.
  • [30] Polat K. "Classification of Parkinson's disease using feature weighting method on the basis of fuzzy C-means lustering". International Journal of Systems Science vol. 43 no.4, pp.597-609, 2012.
  • [31] Benba A., Jilbab A. Hammouch A., "Hybridization of best acoustic cues for detecting persons with Parkinson's disease", 2014 Second World Conference on Complex Systems (WCCS), Agadir, 2014, pp. 622-625.
APA ÇALIŞKAN A, Badem H, Basturk A, YÜKSEL M (2017). DIAGNOSIS OF THE PARKINSON DISEASE BY USING DEEP NEURAL NETWORK CLASSIFIER. , 3311 - 3318.
Chicago ÇALIŞKAN Abdullah,Badem Hasan,Basturk Alper,YÜKSEL MEHMET EMİN DIAGNOSIS OF THE PARKINSON DISEASE BY USING DEEP NEURAL NETWORK CLASSIFIER. (2017): 3311 - 3318.
MLA ÇALIŞKAN Abdullah,Badem Hasan,Basturk Alper,YÜKSEL MEHMET EMİN DIAGNOSIS OF THE PARKINSON DISEASE BY USING DEEP NEURAL NETWORK CLASSIFIER. , 2017, ss.3311 - 3318.
AMA ÇALIŞKAN A,Badem H,Basturk A,YÜKSEL M DIAGNOSIS OF THE PARKINSON DISEASE BY USING DEEP NEURAL NETWORK CLASSIFIER. . 2017; 3311 - 3318.
Vancouver ÇALIŞKAN A,Badem H,Basturk A,YÜKSEL M DIAGNOSIS OF THE PARKINSON DISEASE BY USING DEEP NEURAL NETWORK CLASSIFIER. . 2017; 3311 - 3318.
IEEE ÇALIŞKAN A,Badem H,Basturk A,YÜKSEL M "DIAGNOSIS OF THE PARKINSON DISEASE BY USING DEEP NEURAL NETWORK CLASSIFIER." , ss.3311 - 3318, 2017.
ISNAD ÇALIŞKAN, Abdullah vd. "DIAGNOSIS OF THE PARKINSON DISEASE BY USING DEEP NEURAL NETWORK CLASSIFIER". (2017), 3311-3318.
APA ÇALIŞKAN A, Badem H, Basturk A, YÜKSEL M (2017). DIAGNOSIS OF THE PARKINSON DISEASE BY USING DEEP NEURAL NETWORK CLASSIFIER. Istanbul University Journal of Electrical and Electronics Engineering, 17(2), 3311 - 3318.
Chicago ÇALIŞKAN Abdullah,Badem Hasan,Basturk Alper,YÜKSEL MEHMET EMİN DIAGNOSIS OF THE PARKINSON DISEASE BY USING DEEP NEURAL NETWORK CLASSIFIER. Istanbul University Journal of Electrical and Electronics Engineering 17, no.2 (2017): 3311 - 3318.
MLA ÇALIŞKAN Abdullah,Badem Hasan,Basturk Alper,YÜKSEL MEHMET EMİN DIAGNOSIS OF THE PARKINSON DISEASE BY USING DEEP NEURAL NETWORK CLASSIFIER. Istanbul University Journal of Electrical and Electronics Engineering, vol.17, no.2, 2017, ss.3311 - 3318.
AMA ÇALIŞKAN A,Badem H,Basturk A,YÜKSEL M DIAGNOSIS OF THE PARKINSON DISEASE BY USING DEEP NEURAL NETWORK CLASSIFIER. Istanbul University Journal of Electrical and Electronics Engineering. 2017; 17(2): 3311 - 3318.
Vancouver ÇALIŞKAN A,Badem H,Basturk A,YÜKSEL M DIAGNOSIS OF THE PARKINSON DISEASE BY USING DEEP NEURAL NETWORK CLASSIFIER. Istanbul University Journal of Electrical and Electronics Engineering. 2017; 17(2): 3311 - 3318.
IEEE ÇALIŞKAN A,Badem H,Basturk A,YÜKSEL M "DIAGNOSIS OF THE PARKINSON DISEASE BY USING DEEP NEURAL NETWORK CLASSIFIER." Istanbul University Journal of Electrical and Electronics Engineering, 17, ss.3311 - 3318, 2017.
ISNAD ÇALIŞKAN, Abdullah vd. "DIAGNOSIS OF THE PARKINSON DISEASE BY USING DEEP NEURAL NETWORK CLASSIFIER". Istanbul University Journal of Electrical and Electronics Engineering 17/2 (2017), 3311-3318.