Deviation-based Dynamic Time Warping for Clustering Human Sleep

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

Abstract

In this paper, we propose two versions of a modified dynamic time warping approach for comparing discrete time series. This approach is motivated by the observation that the distribution of dynamic time warping paths between pairs of human sleep time series is concentrated around the path of constant slope. Both versions use a penalty term for the deviation between the warping path and the path of constant slope for a given pair of time series. In the first version, global weighted dynamic time warping, the penalty term is added as a post-processing step after a standard dynamic time warping computation, yielding a modified similarity metric that can be used for time series clustering. The second version, stepwise deviation-based dynamic time warping, incorporates the penalty term into the dynamic programming optimization itself, yielding modified optimal warping paths, together with a similarity metric. Clustering experiments over synthetic data, as well as over human sleep data, show that the proposed methods yield significantly improved accuracy and generative log likelihood as compared with standard dynamic time warping.

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Paper Citation


in Harvard Style

Wang C., Alvarez S., Ruiz C. and Moonis M. (2016). Deviation-based Dynamic Time Warping for Clustering Human Sleep . In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2016) ISBN 978-989-758-170-0, pages 88-95. DOI: 10.5220/0005729200880095


in Bibtex Style

@conference{biosignals16,
author={Chiying Wang and Sergio A. Alvarez and Carolina Ruiz and Majaz Moonis},
title={Deviation-based Dynamic Time Warping for Clustering Human Sleep},
booktitle={Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2016)},
year={2016},
pages={88-95},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005729200880095},
isbn={978-989-758-170-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2016)
TI - Deviation-based Dynamic Time Warping for Clustering Human Sleep
SN - 978-989-758-170-0
AU - Wang C.
AU - Alvarez S.
AU - Ruiz C.
AU - Moonis M.
PY - 2016
SP - 88
EP - 95
DO - 10.5220/0005729200880095