Semi-Markov Modeling-Clustering of Human Sleep with Efficient Initialization and Stopping

Chiying Wang, Sergio A. Alvarez, Carolina Ruiz, Majaz Moonis

2014

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.

Paper Citation

in Harvard Style

Wang C., A. Alvarez S., Ruiz C. and Moonis M. (2014). Semi-Markov Modeling-Clustering of Human Sleep with Efficient Initialization and Stopping . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2014) ISBN 978-989-758-011-6, pages 61-68. DOI: 10.5220/0004824900610068

in Bibtex Style

@conference{biosignals14,
author={Chiying Wang and Sergio A. Alvarez and Carolina Ruiz and Majaz Moonis},
title={Semi-Markov Modeling-Clustering of Human Sleep with Efficient Initialization and Stopping},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2014)},
year={2014},
pages={61-68},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004824900610068},
isbn={978-989-758-011-6},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2014)
TI - Semi-Markov Modeling-Clustering of Human Sleep with Efficient Initialization and Stopping
SN - 978-989-758-011-6
AU - Wang C.
AU - A. Alvarez S.
AU - Ruiz C.
AU - Moonis M.
PY - 2014
SP - 61
EP - 68
DO - 10.5220/0004824900610068