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DIAGNOSIS OF THE PARKINSON DISEASE BY USING DEEP NEURAL NETWORK CLASSIFIER

Year 2017, Volume: 17 Issue: 2, 3311 - 3318, 27.07.2017

Abstract

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.

References

  • D. G. Standaert, M. H. Saint-Hilaire, C. A. Thomas "Parkinson’s Disease Handbook", American Parkinson Disease Association, New York, USA, 2015.
  • J. Jankovic, "Parkinson’s disease: clinical features and diagnosis", Journal of Neurology, Neurosurgery & Psychiatry, vol. 79, no. 4, pp. 368-376, 2008.
Year 2017, Volume: 17 Issue: 2, 3311 - 3318, 27.07.2017

Abstract

References

  • D. G. Standaert, M. H. Saint-Hilaire, C. A. Thomas "Parkinson’s Disease Handbook", American Parkinson Disease Association, New York, USA, 2015.
  • J. Jankovic, "Parkinson’s disease: clinical features and diagnosis", Journal of Neurology, Neurosurgery & Psychiatry, vol. 79, no. 4, pp. 368-376, 2008.
There are 2 citations in total.

Details

Subjects Engineering
Journal Section Articles
Authors

Abdullah Caliskan This is me

Hasan Badem

Alper Basturk

Mehmet Yuksel

Publication Date July 27, 2017
Published in Issue Year 2017 Volume: 17 Issue: 2

Cite

APA Caliskan, A., Badem, H., Basturk, A., Yuksel, M. (2017). DIAGNOSIS OF THE PARKINSON DISEASE BY USING DEEP NEURAL NETWORK CLASSIFIER. IU-Journal of Electrical & Electronics Engineering, 17(2), 3311-3318.
AMA Caliskan A, Badem H, Basturk A, Yuksel M. DIAGNOSIS OF THE PARKINSON DISEASE BY USING DEEP NEURAL NETWORK CLASSIFIER. IU-Journal of Electrical & Electronics Engineering. July 2017;17(2):3311-3318.
Chicago Caliskan, Abdullah, Hasan Badem, Alper Basturk, and Mehmet Yuksel. “DIAGNOSIS OF THE PARKINSON DISEASE BY USING DEEP NEURAL NETWORK CLASSIFIER”. IU-Journal of Electrical & Electronics Engineering 17, no. 2 (July 2017): 3311-18.
EndNote Caliskan A, Badem H, Basturk A, Yuksel M (July 1, 2017) DIAGNOSIS OF THE PARKINSON DISEASE BY USING DEEP NEURAL NETWORK CLASSIFIER. IU-Journal of Electrical & Electronics Engineering 17 2 3311–3318.
IEEE A. Caliskan, H. Badem, A. Basturk, and M. Yuksel, “DIAGNOSIS OF THE PARKINSON DISEASE BY USING DEEP NEURAL NETWORK CLASSIFIER”, IU-Journal of Electrical & Electronics Engineering, vol. 17, no. 2, pp. 3311–3318, 2017.
ISNAD Caliskan, Abdullah et al. “DIAGNOSIS OF THE PARKINSON DISEASE BY USING DEEP NEURAL NETWORK CLASSIFIER”. IU-Journal of Electrical & Electronics Engineering 17/2 (July 2017), 3311-3318.
JAMA Caliskan A, Badem H, Basturk A, Yuksel M. DIAGNOSIS OF THE PARKINSON DISEASE BY USING DEEP NEURAL NETWORK CLASSIFIER. IU-Journal of Electrical & Electronics Engineering. 2017;17:3311–3318.
MLA Caliskan, Abdullah et al. “DIAGNOSIS OF THE PARKINSON DISEASE BY USING DEEP NEURAL NETWORK CLASSIFIER”. IU-Journal of Electrical & Electronics Engineering, vol. 17, no. 2, 2017, pp. 3311-8.
Vancouver Caliskan A, Badem H, Basturk A, Yuksel M. DIAGNOSIS OF THE PARKINSON DISEASE BY USING DEEP NEURAL NETWORK CLASSIFIER. IU-Journal of Electrical & Electronics Engineering. 2017;17(2):3311-8.