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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 U. of Massachusetts Medical School, United States

ISBN: 978-989-758-170-0

Keyword(s): Dynamic Time Warping, Deviation, Human Sleep, Clustering.

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: 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. (More)

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Paper citation in several formats:
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

@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},
}

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

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