TY - GEN
T1 - Characterizing Masseter Surface Electromyography on EEG-Related Frequency Bands in Parkinson’s Disease Neuromotor Dysarthria
AU - Gómez-Rodellar, Andrés
AU - Gómez-Vilda, Pedro
AU - Ferrández-Vicente, JoséManuel M.
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 wish to thank Víctor Lorente for his inspiring thoughts (School of Veterinary, UCM, Spain).
Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022/5/24
Y1 - 2022/5/24
N2 - Speech has proven to be an effective neuromotor biomarker, capitalizing on the capabilities of contact-free technology. This study aims to evaluate the behavior of facial muscles’ activity estimating the entropy of their surface electromyographic (sEMG) activity during the production of diadochokinetic speech tests. The study explores the entropic behavior of the sEMG signal in certain frequency bands associated to EEG activity comparing participants affected by neuromotor diseases than in age-matched normative participants. Using recordings from two PD vs two HC participants on 5 EEG bands (δ, ϑ, α, β, γ ), the maximum entropy estimated on the HC group was 5.70 10 - 5, whereas the minimum entropy on the PD group was 7.25 10 - 5. A hypothesis test rejected the similarity between the PD and HC results with a p-value under 0.0003. This different behavior might open the way to a wider study in characterizing neuromotor disease alterations from neuromotor origin.
AB - Speech has proven to be an effective neuromotor biomarker, capitalizing on the capabilities of contact-free technology. This study aims to evaluate the behavior of facial muscles’ activity estimating the entropy of their surface electromyographic (sEMG) activity during the production of diadochokinetic speech tests. The study explores the entropic behavior of the sEMG signal in certain frequency bands associated to EEG activity comparing participants affected by neuromotor diseases than in age-matched normative participants. Using recordings from two PD vs two HC participants on 5 EEG bands (δ, ϑ, α, β, γ ), the maximum entropy estimated on the HC group was 5.70 10 - 5, whereas the minimum entropy on the PD group was 7.25 10 - 5. A hypothesis test rejected the similarity between the PD and HC results with a p-value under 0.0003. This different behavior might open the way to a wider study in characterizing neuromotor disease alterations from neuromotor origin.
KW - EEG
KW - Entropy
KW - Hypokinetic dysarthria
KW - Neuromotor diseases
KW - Parkinson’s Disease
KW - Surface electromyography
UR - http://www.scopus.com/inward/record.url?scp=85132010300&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-06242-1_22
DO - 10.1007/978-3-031-06242-1_22
M3 - Conference contribution
AN - SCOPUS:85132010300
SN - 9783031062414
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 219
EP - 228
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 -