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Deep Learning for Medical Decision Support Systems pp 73–93Cite as

Diagnosing Parkinson by Using Deep Autoencoder Neural Network

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 909))

Abstract

The deep learning is strong on not only images (as explained in the previous Chap. 4) but also on sound-type data. It is possible to show that in a serious disease called as Parkinson’s disease (PD). PD is a degenerative disease of the central nervous system. As coming after the Alzheimer’s disease, PD is known among critical common neurodegenerative diseases. The number of people with PD worldwide is quite high and is rapidly increasing, especially in countries (developing) in the context of Asia. The Olmsted County (Mayo Clinic) has reported the life-time risk of Parkinson’s disease at 2% for men. That value is 1.3 for women. It has been confirmed in many sources that the incidence of males is higher. It is stated that the number of PD patients will be doubled by 2030. Early diagnosis of PD disease can also reduce symptoms. Significant symptoms of PD are tremor, stiffness, slow motion, motor symptom asymmetry and impaired posture. In addition, phonation and speech disorders are common in the PD patients. As a result, PD is a chronic and progressive disorder of movements, and symptoms become worse over time. It is reported that almost 1 million people living in the US are an age with Parkinson’s disease.

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References

  1. Z. Cai, J. Gu, C. Wen, D. Zhao, C. Huang, H. Huang, C. Tong, J. Li, H. Chen, An intelligent Parkinson’s disease diagnostic system based on a chaotic bacterial foraging optimization enhanced fuzzy KNN approach. Comput. Math. Methods Med. 2018, 24 (2018)

    Article  Google Scholar 

  2. D. Gil, D.J. Manuel, Diagnosing Parkinson by using artificial neural networks and support vector machines. Glob. J. Comput. Sci. Technol. 9(4), 63–71 (2009)

    Google Scholar 

  3. A. Elbaz, J.H. Bower, D.M. Maraganore, S.K. McDonnell, B.J. Peterson, J.E. Ahlskog, D.J. Schaid, W.A. Rocca, Risk tables for Parkinsonism and Parkinson’s disease. J. Clin. Epidemiol. 55(1), 25–31 (2002)

    Article  Google Scholar 

  4. E. Dorsey, R. Constantinescu, J.P. Thompson, K.M. Biglan, R.G. Holloway, K. Kieburtz, F.J. Marshall, B.M. Ravina, G. Schifitto, A. Siderowf, C.M. Tanner, Projected number of people with Parkinson disease in the most populous nations, 2005 through 2030. Neurology 68(5), 384–386 (2007)

    Article  Google Scholar 

  5. R.G. Ramani, G. Sivagami, Parkinson disease classification using data mining algorithms. Int. J. Comput. Appl. 32(9), 17–22 (2011)

    Google Scholar 

  6. Parkinson’s Disease Foundation, https://www.parkinson.org/. Last access 04 Jan 2020

  7. K. Revett, F. Gorunescu, A.B.M. Salem, Feature selection in Parkinson’s disease: a rough sets approach, in 2009 International Multiconference on Computer Science and Information Technology (IEEE, 2009), pp. 425–428

    Google Scholar 

  8. S.S. Rao, L.A. Hofmann, A. Shakil, Parkinson’s disease: diagnosis and treatment. Am. Fam. Phys. 74(12), 2046–2054 (2006)

    Google Scholar 

  9. M. Ene, Neural network-based approach to discriminate healthy people from those with Parkinson’s disease. Ann. Univ. Craiova Math. Comput. Sci. Ser. 35, 112–116 (2008)

    MathSciNet  MATH  Google Scholar 

  10. M. Gil-Martín, J.M. Montero, R. San-Segundo, Parkinson’s disease detection from drawing movements using convolutional neural networks. Electronics 8(8), 907 (2019)

    Article  Google Scholar 

  11. C.R. Pereira, S.A. Weber, C. Hook, G.H. Rosa, J.P. Papa, Deep learning-aided Parkinson’s disease diagnosis from handwritten dynamics, in 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) (IEEE, 2016), pp. 340–346

    Google Scholar 

  12. S.L. Oh, Y. Hagiwara, U. Raghavendra, R. Yuvaraj, N. Arunkumar, M. Murugappan, Acharya, U.R. Murugappan, A deep learning approach for Parkinson’s disease diagnosis from EEG signals. Neural Comput. Appl. 1–7 (2018)

    Google Scholar 

  13. H. Choi, S. Ha, H.J. Im, S.H. Paek, D.S. Lee, Refining diagnosis of Parkinson’s disease with deep learning-based interpretation of dopamine transporter imaging. Neuroimage Clin. 16, 586–594 (2017)

    Article  Google Scholar 

  14. B.M. Eskofier, S.I. Lee, J.F. Daneault, F.N. Golabchi, G. Ferreira-Carvalho, G. Vergara-Diaz, S. Sapienza, G. Costante, J. Klucken, T. Kautz, P. Bonato, Recent machine learning advancements in sensor-based mobility analysis: deep learning for Parkinson’s disease assessment, in 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (IEEE, 2016), pp. 655–658

    Google Scholar 

  15. S. Sivaranjini, C.M. Sujatha, Deep learning based diagnosis of Parkinson’s disease using convolutional neural network. Multimed. Tools Appl. 1–13 (2019)

    Google Scholar 

  16. S. Shinde, S. Prasad, Y. Saboo, R. Kaushick, J. Saini, P.K. Pal, M. Ingalhalikar, Predictive markers for Parkinson’s disease using deep neural nets on neuromelanin sensitive MRI. Neuroimage Clin. 22, 101748 (2019)

    Article  Google Scholar 

  17. A. Ortiz, J. Munilla, M. Martínez, J.M. Gorriz, J. Ramírez, D. Salas-Gonzalez, Parkinson’s disease detection using isosurfaces-based features and convolutional neural networks. Front. Neuroinf. 13, 48 (2019)

    Article  Google Scholar 

  18. S. Grover, S. Bhartia, A. Yadav, K.R. Seeja, Predicting severity of Parkinson’s disease using deep learning. Proc. Comput, Sci. 132, 1788–1794 (2018)

    Article  Google Scholar 

  19. S. Muthumanickam, J. Gayathri, Daphne J. Eunice, Parkinson’s disease detection and classification using machine learning and deep learning algorithms—a survey. Int. J. Eng. Sci. Invent. (IJESI) 7(5), 56–63 (2018)

    Google Scholar 

  20. A.A. Spadoto, R.C. Guido, F.L. Carnevali, A.F. Pagnin, A.X. Falcão, J.P. Papa, Improving Parkinson’s disease identification through evolutionary-based feature selection, in 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE, 2011), pp. 7857–7860

    Google Scholar 

  21. D. Karunanithi, P. Rodrigues, Diagnosis of Parkinson’s disease using fuzzy height. Int. J. Pure Appl. Math. 118(20), 4497–4501 (2018)

    Google Scholar 

  22. R.F. Olanrewaju, N.S. Sahari, A.A. Musa, N. Hakiem, Application of neural networks in early detection and diagnosis of Parkinson’s disease, in 2014 International Conference on Cyber and IT Service Management (CITSM) (IEEE, 2014), pp. 78–82

    Google Scholar 

  23. P. Durga, V.S. Jebakumari, D. Shanthi, Diagnosis and classification of Parkinsons disease using data mining techniques. Int. J. Adv. Res. Trends Eng. Technol. 3, 86–90 (2016)

    Google Scholar 

  24. Y.N. Zhang, Can a smartphone diagnose parkinson disease? A deep neural network method and telediagnosis system implementation, in Parkinson’s Disease (2017)

    Google Scholar 

  25. M.A. Little, P.E. McSharry, S.J. Roberts, D.A. Costello, I.M. Moroz, Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection. Biomed. Eng. Online 6(1), 23 (2007)

    Article  Google Scholar 

  26. M. Little, P. McSharry, E. Hunter, J. Spielman, L. Ramig, Suitability of dysphonia measurements for telemonitoring of Parkinson’s disease. Nat. Preced. 1 (2008)

    Google Scholar 

  27. Oxford Parkinson’s Disease Detection Dataset, https://archive.ics.uci.edu/ml/datasets/parkinsons. Last access 09 Jan 2020

  28. R. Geetha Ramani, G. Sivagami, Parkinson disease classification using data mining algorithms. Int. J. Comput. Appl. 32(9) (2011) (0975-8887)

    Google Scholar 

  29. X. Wang, Data mining analysis of the Parkinson’s disease. Mathematics Theses, Georgia State University, 17 Feb 2014

    Google Scholar 

  30. W. Liu, Z. Wang, X. Liu, N. Zeng, Y. Liu, F.E. Alsaadi, A survey of deep neural network architectures and their applications. Neurocomputing 234, 11–26 (2017)

    Article  Google Scholar 

  31. Y. Bengio, P. Lamblin, D. Popovici, H. Larochelle, Greedy layer-wise training of deep networks. Adv. Neural. Inf. Process. Syst. 19, 153–160 (2007)

    Google Scholar 

  32. Y. Chen, Z. Lin, X. Zhao, G. Wang, Y. Gu, Deep learning-based classification of hyperspectral data. IEEE J. Sel. Top. Appl. Earth Observ. Rem. Sens. 7(6), 2094–2107 (2014)

    Article  Google Scholar 

  33. W. Zhang, J. Han, S. Deng, Heart sound classification based on scaled spectrogram and tensor decomposition. Biomed. Signal Process. Control 32, 20–28 (2017)

    Article  Google Scholar 

  34. R.P. Espíndola, N.F.F. Ebecken, On extending F-measure and G-mean metrics to multi-class problems. WIT Trans. Inf. Commun. Technol. 35

    Google Scholar 

  35. O. Deperlioglu, Classification of phonocardiograms with convolutional neural networks. BRAIN Broad Res. Artif. Intell. Neurosci. 9(2), 23–33 (2018)

    Google Scholar 

  36. D.J. Hemanth, O. Deperlioglu, U. Kose, An enhanced diabetic retinopathy detection and classification approach using deep convolutional neural network. Neural Comput. Appl. 32, 1–15 (2020)

    Article  Google Scholar 

  37. Q.-A. Mubarak, M.U. Akram, A. Shaukat, F. Hussain, S.G. Khawaja, W.H. Butt, Analysis of PCG signals using quality assessment and homomorphic filters for localization and classification of heart sounds. Comput. Methods Program. Biomed. (2018). https://doi.org/10.1016/j.cmpb.2018.07.006

    Article  Google Scholar 

  38. L. Deng, D. Yu, Deep learning: methods and applications. Found. Trends Sig. Process. 7(3–4), 197–387 (2014)

    Article  MathSciNet  Google Scholar 

  39. I. Goodfellow, Y. Bengio, A. Courville, in Deep Learning (MIT Press, 2016)

    Google Scholar 

  40. Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nature 521(7553), 436–444 (2015)

    Article  Google Scholar 

  41. Deep Learning Tutorial, in Release 0.1, LISA Lab. University of Montreal, Sept 2015

    Google Scholar 

  42. A. Zhang, Z.C. Lipton, M. Li, A.J. Smola, Dive into deep learning, in Unpublished Draft. Retrieved, Mar 2019, p. 319

    Google Scholar 

  43. P. Baldi, Autoencoders, unsupervised learning, and deep architectures, in Proceedings of ICML Workshop on Unsupervised and Transfer Learning, June 2012, pp. 37–49

    Google Scholar 

  44. Q.V. Le, A tutorial on deep learning part 2: autoencoders, convolutional neural networks and recurrent neural networks. Google Brain 1–20 (2015)

    Google Scholar 

  45. S. Amiriparian, M. Freitag, N. Cummins, B. Schuller, Sequence to sequence autoencoders for unsupervised representation learning from audio, in Proceedings of the DCASE 2017 Workshop, Nov 2017

    Google Scholar 

  46. O. Deperlioglu, Classification of segmented heart sounds with autoencoder neural networks, in VIII. International Multidisciplinary Congress of Eurasia (IMCOFE’2019) (Antalya, 2019), 24–26 Apr 2019, pp. 122–128. ISBN: 978-605-68882-6-7

    Google Scholar 

  47. O. Deperlioglu, Hepatitis disease diagnosis with deep neural networks, in International 4th European Conference on Science, Art & Culture (ECSAC’2019) (Antalya, 2019), 18–21 Apr 2019, pp. 467–473. ISBN: 978-605-7809-73-5

    Google Scholar 

  48. O. Deperlioglu, Using autoencoder deep neural networks for diagnosis of breast cancer, in International 4th European Conference on Science, Art & Culture (ECSAC’2019) (Antalya, 2019), 18–21 Apr 2019, pp. 475–481. ISBN: 978-605-7809-73-5

    Google Scholar 

  49. B. Xia, C. Bao, Wiener filtering based speech enhancement with weighted denoising auto-encoder and noise classification. Speech Commun. 60, 13–29 (2014)

    Article  Google Scholar 

  50. K. Noda, Y. Yamaguchi, K. Nakadai, H.G. Okuno, T. Ogata, Audio-visual speech recognition using deep learning. Appl. Intell. 42(4), 722–737 (2015)

    Article  Google Scholar 

  51. R.G. Malkin, A. Waibel, Classifying user environment for mobile applications using linear autoencoding of ambient audio, in Proceedings (ICASSP’05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005, vol. 5 (IEEE, 2005), pp v–509

    Google Scholar 

  52. M. Nilashi, O. Ibrahim, A. Ahani, Accuracy improvement for predicting Parkinson’s disease progression. Sci. Rep. 6, 34181 (2016)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Süleyman Demirel University, Isparta, Turkey

    Utku Kose

  2. Department of Computer Technologies, Afyon Kocatepe University, Afyonkarahisar, Turkey

    Omer Deperlioglu

  3. Faculty of Engineering, Al-Balqa Applied University, Al-Salt, Jordan

    Jafar Alzubi

  4. Faculty of Computer Science, Alexandru Ioan Cuza University of Iasi, Iasi, Romania

    Bogdan Patrut

Authors
  1. Utku Kose
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  2. Omer Deperlioglu
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  3. Jafar Alzubi
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  4. Bogdan Patrut
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Correspondence to Utku Kose .

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Kose, U., Deperlioglu, O., Alzubi, J., Patrut, B. (2021). Diagnosing Parkinson by Using Deep Autoencoder Neural Network. In: Deep Learning for Medical Decision Support Systems. Studies in Computational Intelligence, vol 909. Springer, Singapore. https://doi.org/10.1007/978-981-15-6325-6_5

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