Semi-Markov Modeling-Clustering of Human Sleep with Efficient Initialization and Stopping
Chiying Wang, Sergio A. Álvarez, Carolina Ruiz, Majaz Moonis · 2014
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.