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

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.

• Autores: Ríos Urrego, Cristian David; Moreno Acevedo, Santiago Andrés; Nöth, Elmar; Orozco Arroyave, Juan Rafael
• Tipo de recurso: http://purl.org/coar/resource_type/c_5794
• Fecha de publicación: 2022
• Institución: Universidad de Antioquia
• Repositorio: Repositorio UdeA
• Idioma: eng
• OAI Identifier: oai:bibliotecadigital.udea.edu.co:10495/34247
• Acceso en línea: https://hdl.handle.net/10495/34247
• Palabra clave: Parkinson Disease; Enfermedad de Parkinson; Speech Recognition Software; Software de Reconocimiento del Habla; Memoria a Corto Plazo; Memory, Short-Term; Redes neurales (computadores); Neural networks (Computer science)
• Rights: openAccess
• License: https://creativecommons.org/licenses/by-nc-nd/4.0/