Authors:
Chiying Wang
1
;
Sergio A. Alvarez
2
;
Carolina Ruiz
1
and
Majaz Moonis
3
Affiliations:
1
Worcester Polytechnic Institute, United States
;
2
Boston College, United States
;
3
University of Massachusetts Medical School, United States
Keyword(s):
Time Series, Dynamic Time Warping, Data Mining: Clustering, Modeling, Markov, Sleep.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Physiological Processes and Bio-Signal Modeling, Non-Linear Dynamics
;
Sensor Networks
;
Soft Computing
Abstract:
Collective Dynamical Modeling-Clustering (CDMC) is an algorithmic framework for time series dynamical
modeling and clustering using probabilistic state-transition models. In this paper, an efficient initialization
technique based on Itakura slope-constrained Dynamic Time Warping is applied to CDMC. Semi-Markov
chains are used as the dynamical models. Experimental evaluation demonstrates the effectiveness of the proposed
approach in providing more realistic dynamical modeling of sleep stage dynamics than Markov models,
with improved clustering quality and convergence speed as compared with pseudorandom initialization.