Deep Learning Approach to Parkinson's Disease Detection Using Voice Recordings and Convolutional Neural Network Dedicated to Image Classification

Wodzinski M, Skalski A, Hemmerling D, Orozco-Arroyave JR, Nöth E (2019)

Publication Type: Conference contribution

Publication year: 2019

Journal

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society

Publisher: Institute of Electrical and Electronics Engineers Inc.

Pages Range: 717-720

Conference Proceedings Title: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

Event location: Berlin DE

ISBN: 9781538613115

DOI: 10.1109/EMBC.2019.8856972

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.

Authors with CRIS profile

Elmar Nöth Professur für Informatik (Mustererkennung)

Involved external institutions

University of Science and Technology (AGH) / Akademia Górniczo-Hutnicza PL Poland (PL) Universidad de Antioquía (UDEA) CO Colombia (CO)

How to cite

APA:

Wodzinski, M., Skalski, A., Hemmerling, D., Orozco-Arroyave, J.R., & Nöth, E. (2019). Deep Learning Approach to Parkinson's Disease Detection Using Voice Recordings and Convolutional Neural Network Dedicated to Image Classification. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (pp. 717-720). Berlin, DE: Institute of Electrical and Electronics Engineers Inc..

MLA:

Wodzinski, Marek, et al. "Deep Learning Approach to Parkinson's Disease Detection Using Voice Recordings and Convolutional Neural Network Dedicated to Image Classification." Proceedings of the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019, Berlin Institute of Electrical and Electronics Engineers Inc., 2019. 717-720.

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