TY - GEN
T1 - Multiscale evolution of attractor-shape descriptors for assessing Parkinson's disease severity
AU - Som, Anirudh
AU - Krishnamurthi, Narayanan
AU - Venkataraman, Vinay
AU - Ramamurthy, Karthikeyan Natesan
AU - Turaga, Pavan
N1 - Funding Information:
This research was supported by NSF CAREER award 1452163, and by the research grant from National Institute of Child Health and Human Development, National Institutes of Health (1R21HD060315) which supported the data collection from people with Parkinson’s disease that were analyzed in this study.
PY - 2018/3/7
Y1 - 2018/3/7
N2 - We propose a nonparametric framework for analyzing and modeling dynamic postural shifts of human subjects. The postural shifts are represented in the phase space using time-delay embeddings and novel shape-theoretic features are extracted. The proposed multiscale descriptors are used as discriminative features to differentiate dynamical systems. The descriptors are simple and easy to compute, and model the multiscale characteristics of the attractor's multi-dimensional shape measurements. We demonstrate the usefulness of these features by using them to classify subjects into healthy and those affected by Parkinson's disease. We also use these features to assess the severity of the disease. In all these use cases, the proposed multiscale features perform better than their global counterparts.
AB - We propose a nonparametric framework for analyzing and modeling dynamic postural shifts of human subjects. The postural shifts are represented in the phase space using time-delay embeddings and novel shape-theoretic features are extracted. The proposed multiscale descriptors are used as discriminative features to differentiate dynamical systems. The descriptors are simple and easy to compute, and model the multiscale characteristics of the attractor's multi-dimensional shape measurements. We demonstrate the usefulness of these features by using them to classify subjects into healthy and those affected by Parkinson's disease. We also use these features to assess the severity of the disease. In all these use cases, the proposed multiscale features perform better than their global counterparts.
KW - Multiscale
KW - Parkinson's Disease Severity Assessment
KW - Reconstructed Attractor
KW - Shape Distributions
UR - http://www.scopus.com/inward/record.url?scp=85048048318&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048048318&partnerID=8YFLogxK
U2 - 10.1109/GlobalSIP.2017.8309098
DO - 10.1109/GlobalSIP.2017.8309098
M3 - Conference contribution
AN - SCOPUS:85048048318
T3 - 2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings
SP - 938
EP - 942
BT - 2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017
Y2 - 14 November 2017 through 16 November 2017
ER -