DIAGNOSIS OF THE PARKINSON DISEASE BY USING  DEEP NEURAL NETWORK CLASSIFIER

Abdullah Caliskan; Hasan Badem; Alper BASTURK; Mehmet YUKSEL

Makale detayları

DIAGNOSIS OF THE PARKINSON DISEASE BY USING DEEP NEURAL NETWORK CLASSIFIER

Yazarlar:

Abdullah Caliskan | Hasan Badem | Alper BASTURK | Mehmet YUKSEL

Yayıncı:

İstanbul University-Cerrahpasa

Ana Başlıklar:

Uygulamalı fizik

Dergi:

IU-Journal of Electrical & Electronics Engineering 2017 Vol.17 Issue 2, pp.3311-3318

Belge Tipi:

Makale

Yayın Tarihi:

7/2017

Belge Dili:

Türkçe

Document ID (Org No.):

2000777731

Sıklık:

İki Yıllık

eISSN:

2619-9831

Özet:

 

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.

Makale detayları

DIAGNOSIS OF THE PARKINSON DISEASE BY USING DEEP NEURAL NETWORK CLASSIFIER

Yazarlar: Abdullah Caliskan | Hasan Badem | Alper BASTURK | Mehmet YUKSEL

Yayıncı: İstanbul University-Cerrahpasa

Ana Başlıklar: Uygulamalı fizik

Dergi: IU-Journal of Electrical & Electronics Engineering 2017 Vol.17 Issue 2, pp.3311-3318

Belge Tipi: Makale

Yayın Tarihi: 7/2017

Belge Dili: Türkçe

Document ID (Org No.): 2000777731

Sıklık: İki Yıllık

eISSN: 2619-9831

Sayı

Yazarlar:

Abdullah Caliskan | Hasan Badem | Alper BASTURK | Mehmet YUKSEL

Yayıncı:

İstanbul University-Cerrahpasa

Ana Başlıklar:

Uygulamalı fizik

Dergi:

IU-Journal of Electrical & Electronics Engineering 2017 Vol.17 Issue 2, pp.3311-3318

Belge Tipi:

Makale

Yayın Tarihi:

7/2017

Belge Dili:

Türkçe

Document ID (Org No.):

2000777731

Sıklık:

İki Yıllık

eISSN:

2619-9831