Proceedings ArticleDOI
Deep Learning Approach to Parkinson’s Disease Detection Using Voice Recordings and Convolutional Neural Network Dedicated to Image Classification
Marek Wodzinski,Andrzej Skalski,Daria Hemmerling,Juan Rafael Orozco-Arroyave,Elmar Nöth +4 more
- Vol. 2019, pp 717-720
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TLDR
It turned out that features learned on natural images are able to transfer the knowledge to artificial images representing the spectrogram of the voice signal, and it was shown that it is possible to perform a successful detection of Parkinson’s disease using only frequency-based features.Abstract:
This study presents an approach to Parkinson’s disease detection using vowels with sustained phonation and a ResNet architecture dedicated originally to image classification. We calculated spectrum of the audio recordings and used them as an image input to the ResNet architecture pre-trained using the ImageNet and SVD databases. To prevent overfitting the dataset was strongly augmented in the time domain. The Parkinson’s dataset (from PC-GITA database) consists of 100 patients (50 were healthy / 50 were diagnosed with Parkinson’s disease). Each patient was recorded 3 times. The obtained accuracy on the validation set is above 90% which is comparable to the current state-of-the-art methods. The results are promising because it turned out that features learned on natural images are able to transfer the knowledge to artificial images representing the spectrogram of the voice signal. What is more, we showed that it is possible to perform a successful detection of Parkinson’s disease using only frequency-based features. A spectrogram enables visual representation of frequencies spectrum of a signal. It allows to follow the frequencies changes of a signal in time.read more
Citations
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Journal ArticleDOI
An Essay on the Shaking Palsy
TL;DR: In this paper, the authors present a conciliatory explanation for the present publication, in which, it is acknowledged, that mere conjecture takes the place of experiment; and, that analogy is the substitute for anatomical examination, the only sure foundation for pathological knowledge.
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Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature.
TL;DR: A comprehensive overview of data modalities and machine learning methods that have been used in the diagnosis and differential diagnosis of Parkinson's disease is provided in this paper, where a literature review of studies published until February 14, 2020, using the PubMed and IEEE Xplore databases.
Journal ArticleDOI
A Deep Learning Based Method for Parkinson’s Disease Detection Using Dynamic Features of Speech
TL;DR: In this paper, a comparative analysis of the articulation transition characteristics shows that the number of articulation transitions and the trend of the fundamental frequency curve are significantly different between HC speakers and PD patients.
Journal ArticleDOI
Application of Deep Learning Models for Automated Identification of Parkinson's Disease: A Review (2011-2021).
Hui Wen Loh,Wanrong Hong,Chui Ping Ooi,Subrata Chakraborty,Prabal Datta Barua,Prabal Datta Barua,Ravinesh C. Deo,Jeffrey Soar,Elizabeth E. Palmer,U. Rajendra Acharya +9 more
TL;DR: In this article, the authors identified 63 studies published between January 2011 and July 2021, that proposed deep learning models for an automated diagnosis of Parkinson's disease, using various types of modalities like brain analysis (SPECT, PET, MRI and EEG), and motion symptoms (gait, handwriting, speech and EMG).
Journal ArticleDOI
The Role of Neural Network for the Detection of Parkinson's Disease: A Scoping Review.
Mahmood Saleh Alzubaidi,Uzair Shah,Haider Dhia Zubaydi,Khalid Dolaat,Alaa Abd-Alrazaq,Arfan Ahmed,Mowafa Househ +6 more
TL;DR: In this article, the authors explored and summarized the applications of neural networks to diagnose Parkinson's disease and found that neural networks play an integral and substantial role in combating Parkinson's Disease.
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TL;DR: The combined clinimetric results of this study support the validity of the MDS‐UPDRS for rating PD.
Posted Content
Recurrent Models of Visual Attention
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