End-to-End Parkinson’s Disease Detection Using a Deep Convolutional Recurrent Network

Rios-Urrego CD, Moreno-Acevedo SA, Nöth E, Orozco Arroyave JR (2022)

Publication Type: Conference contribution

Publication year: 2022

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 13502 LNAI

Pages Range: 326-338

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Brno CZ

ISBN: 9783031162695

DOI: 10.1007/978-3-031-16270-1_27

Abstract

Deep Learning (DL) has enabled the development of accurate computational models to evaluate and monitor the neurological state of different disorders including Parkinson’s Disease (PD). Although researchers have used different DL architectures including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM) units, fully connected networks, combinations of them, and others, but few works have correctly analyzed and optimized the input size of the network and how the network processes the information. This study proposes the classification of patients suffering from PD vs. healthy subjects using a 1D CNN followed by an LSTM. We show how the network behaves when its input and the kernel size in different layers are modified. In addition, we evaluate how the network discriminates between PD patients and healthy controls based on several speech tasks. The fusion of tasks yielded the best results in the classification experiments and showed promising results when classifying patients in different stages of the disease, which suggests the introduced approach is suitable to monitor the disease progression.

Authors with CRIS profile

Elmar Nöth Professur für Informatik (Mustererkennung) Juan Rafael Orozco Arroyave Lehrstuhl für Informatik 14 (Bild- und Sprachverarbeitung)

Involved external institutions

Universidad de Antioquía (UDEA) CO Colombia (CO)

How to cite

APA:

Rios-Urrego, C.D., Moreno-Acevedo, S.A., Nöth, E., & Orozco Arroyave, J.R. (2022). End-to-End Parkinson’s Disease Detection Using a Deep Convolutional Recurrent Network. In Petr Sojka, Aleš Horák, Ivan Kopeček, Karel Pala (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 326-338). Brno, CZ: Springer Science and Business Media Deutschland GmbH.

MLA:

Rios-Urrego, Cristian David, et al. "End-to-End Parkinson’s Disease Detection Using a Deep Convolutional Recurrent Network." Proceedings of the 25th International Conference on Text, Speech, and Dialogue, TSD 2022, Brno Ed. Petr Sojka, Aleš Horák, Ivan Kopeček, Karel Pala, Springer Science and Business Media Deutschland GmbH, 2022. 326-338.

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