TY - GEN
T1 - Characterization of Hypokinetic Dysarthria by a CNN Based on Auditory Receptive Fields
AU - Gómez-Vilda, Pedro
AU - Gómez-Rodellar, Andrés
AU - Palacios-Alonso, Daniel
AU - Álvarez-Marquina, Agustín
AU - Tsanas, Athanasios
N1 - Funding Information:
This research received funding from grants TEC2016-77791-C4-4-R (Ministry of Economic Affairs and Competitiveness of Spain), and Teca-Park-MonParLoc FGCSIC-CENIE 0348-CIE-6-E (InterReg Programme). The authors want to thank the APARKAM association of Parkinson’s Disease patients of Alcorcón and Leganés in Madrid, and the voluntary participants for contributing to this initiative.
Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022/5/24
Y1 - 2022/5/24
N2 - Parkinson’s Disease (PD) is a major neurodegenerative disorder with steadily increasing incidence rates, demanding overgrowing resources from national health systems and imposing considerable burden on caregivers. Cost-effective and efficient turn-around time monitoring methods are required to facilitate regular, longitudinal, accurate clinical assessment and symptom management. Speech has proven to be an effective neuromotor biomarker, capitalizing on the capabilities of contact-free technology. This study aims to evaluate processing speech from people diagnosed with Parkinson’s Disease using Convolutional Neural Networks (CNN) towards characterizing speech articulation kinematics to explore differences between Healthy Controls (HC) and PD participants with Hypokinetic Dysarthria (HD), using Auditory Receptive Fields (ARFs) in the convolutional layers. The proposed proof of concept is based on a CNN described in detail, using an Extreme Learning Machine (ELM) at the output projection layer. This structure is evaluated on speech recordings from 6 PD and 6 HC participants. The performance of the approach is evaluated in terms of correlation and the log-likelihood ratio on the softmax output, showing the efficiency and retrieving properties of the CNN on speech auditory images, towards providing new insights on the pathophysiology of PD speech.
AB - Parkinson’s Disease (PD) is a major neurodegenerative disorder with steadily increasing incidence rates, demanding overgrowing resources from national health systems and imposing considerable burden on caregivers. Cost-effective and efficient turn-around time monitoring methods are required to facilitate regular, longitudinal, accurate clinical assessment and symptom management. Speech has proven to be an effective neuromotor biomarker, capitalizing on the capabilities of contact-free technology. This study aims to evaluate processing speech from people diagnosed with Parkinson’s Disease using Convolutional Neural Networks (CNN) towards characterizing speech articulation kinematics to explore differences between Healthy Controls (HC) and PD participants with Hypokinetic Dysarthria (HD), using Auditory Receptive Fields (ARFs) in the convolutional layers. The proposed proof of concept is based on a CNN described in detail, using an Extreme Learning Machine (ELM) at the output projection layer. This structure is evaluated on speech recordings from 6 PD and 6 HC participants. The performance of the approach is evaluated in terms of correlation and the log-likelihood ratio on the softmax output, showing the efficiency and retrieving properties of the CNN on speech auditory images, towards providing new insights on the pathophysiology of PD speech.
KW - Auditory receptive fields
KW - Convolutional neural networks
KW - Extreme learning machines
KW - Hypokinetic dysarthria kinematics
KW - Parkinson’s disease
UR - http://www.scopus.com/inward/record.url?scp=85132030015&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-06242-1_34
DO - 10.1007/978-3-031-06242-1_34
M3 - Conference contribution
AN - SCOPUS:85132030015
SN - 9783031062414
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 343
EP - 352
BT - Artificial Intelligence in Neuroscience
A2 - Ferrández Vicente, José Manuel
A2 - Álvarez-Sánchez, José Ramón
A2 - de la Paz López, Félix
A2 - Adeli, Hojjat
PB - Springer
T2 - 9th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2022
Y2 - 31 May 2022 through 3 June 2022
ER -