TY - GEN
T1 - Multimodal classification of Parkinson's disease using delay differential analysis
AU - Weyhenmeyer, Jonathan
AU - Hernandez, Manuel E.
AU - Lainscsek, Claudia
AU - Poizner, Howard
AU - Sejnowski, Terrence J.
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12/16
Y1 - 2020/12/16
N2 - Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder in the world. PD is known to lead to marked alterations in cortico-thalamo-basal ganglia activity and subsequent movements, which may provide a biomarker for PD diagnosis. Delay differential analysis (DDA) is a time domain analysis framework based on embedding theory in nonlinear dynamics. An embedding reveals the nonlinear invariant properties of an unknown dynamical system (here the brain) from a single time series electroencephalography (EEG) or behavioral signals. The DDA models serve as a low-dimensional nonlinear functional basis onto which the data are mapped. The combination of behavioral and neurological observations gives rise to a multimodal analysis framework that could improve our understanding and classification of PD using time series data from physical systems. We demonstrate how 750 ms of multimodal data can be used to improve DDA classification performance of PD, over clean EEG or behavioral time series data on their own, in two distinct virtual reach to grasp tasks in an uncertain and dynamic virtual reality environment. Thus, multimodal DDA may provide a tool for aiding the clinician in the diagnosis of PD and bolster classification performance through the combination of a wide array of neural or behavioral signals.
AB - Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder in the world. PD is known to lead to marked alterations in cortico-thalamo-basal ganglia activity and subsequent movements, which may provide a biomarker for PD diagnosis. Delay differential analysis (DDA) is a time domain analysis framework based on embedding theory in nonlinear dynamics. An embedding reveals the nonlinear invariant properties of an unknown dynamical system (here the brain) from a single time series electroencephalography (EEG) or behavioral signals. The DDA models serve as a low-dimensional nonlinear functional basis onto which the data are mapped. The combination of behavioral and neurological observations gives rise to a multimodal analysis framework that could improve our understanding and classification of PD using time series data from physical systems. We demonstrate how 750 ms of multimodal data can be used to improve DDA classification performance of PD, over clean EEG or behavioral time series data on their own, in two distinct virtual reach to grasp tasks in an uncertain and dynamic virtual reality environment. Thus, multimodal DDA may provide a tool for aiding the clinician in the diagnosis of PD and bolster classification performance through the combination of a wide array of neural or behavioral signals.
KW - Electroencephalography
KW - Multiple signal classification
KW - Parkinson's disease
UR - http://www.scopus.com/inward/record.url?scp=85100337485&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85100337485&partnerID=8YFLogxK
U2 - 10.1109/BIBM49941.2020.9313394
DO - 10.1109/BIBM49941.2020.9313394
M3 - Conference contribution
AN - SCOPUS:85100337485
T3 - Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020
SP - 2868
EP - 2875
BT - Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020
A2 - Park, Taesung
A2 - Cho, Young-Rae
A2 - Hu, Xiaohua Tony
A2 - Yoo, Illhoi
A2 - Woo, Hyun Goo
A2 - Wang, Jianxin
A2 - Facelli, Julio
A2 - Nam, Seungyoon
A2 - Kang, Mingon
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020
Y2 - 16 December 2020 through 19 December 2020
ER -