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
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.
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