Top-down approaches to abstract medical time series using linear segments

This work attempts to abstract medical time series using a minimum number of linear segments such that the integral square error between the abstraction and the data is minimum. The problem is difficult since it involves a multiobjective optimization procedure, and the optimization process is affect...

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Main Authors: Sarkar, M., Tze-Yun LEONG
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Language:English
Published: Institutional Knowledge at Singapore Management University 2001
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Online Access:https://ink.library.smu.edu.sg/sis_research/3046
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spelling sg-smu-ink.sis_research-40462016-02-05T06:30:05Z Top-down approaches to abstract medical time series using linear segments Sarkar, M. Tze-Yun LEONG, This work attempts to abstract medical time series using a minimum number of linear segments such that the integral square error between the abstraction and the data is minimum. The problem is difficult since it involves a multiobjective optimization procedure, and the optimization process is affected by the presence of local minima, noise and outliers. This work proposes a greedy approach, which exploits the local and global information for the optimization. Initially, the number of linear segments needed is estimated roughly by detecting the number of cycles in the data set. Then the tendency of each data point to form bends is measured locally in terms of typicality values. A global consensus in terms of clustering is used to select the breakpoints from all the data points with various typicality values. These breakpoints are utilized to partition the data set. Approximating each partition with a linear segment subsequently forms a crude abstraction. The difference between the original data set and the crude abstraction is exploited as the feedback information such that the crude abstraction can be split further for refinement. The efficacy of the proposed method is demonstrated on some real life intensive care unit (ICU) data sets. 2001-10-10T07:00:00Z text https://ink.library.smu.edu.sg/sis_research/3046 info:doi/10.1109/ICSMC.2001.973007 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Abstraction Approximation ICU and medicine Segmentation Time series Numerical Analysis and Scientific Computing
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Abstraction
Approximation
ICU and medicine
Segmentation
Time series
Numerical Analysis and Scientific Computing
spellingShingle Abstraction
Approximation
ICU and medicine
Segmentation
Time series
Numerical Analysis and Scientific Computing
Sarkar, M.
Tze-Yun LEONG,
Top-down approaches to abstract medical time series using linear segments
description This work attempts to abstract medical time series using a minimum number of linear segments such that the integral square error between the abstraction and the data is minimum. The problem is difficult since it involves a multiobjective optimization procedure, and the optimization process is affected by the presence of local minima, noise and outliers. This work proposes a greedy approach, which exploits the local and global information for the optimization. Initially, the number of linear segments needed is estimated roughly by detecting the number of cycles in the data set. Then the tendency of each data point to form bends is measured locally in terms of typicality values. A global consensus in terms of clustering is used to select the breakpoints from all the data points with various typicality values. These breakpoints are utilized to partition the data set. Approximating each partition with a linear segment subsequently forms a crude abstraction. The difference between the original data set and the crude abstraction is exploited as the feedback information such that the crude abstraction can be split further for refinement. The efficacy of the proposed method is demonstrated on some real life intensive care unit (ICU) data sets.
format text
author Sarkar, M.
Tze-Yun LEONG,
author_facet Sarkar, M.
Tze-Yun LEONG,
author_sort Sarkar, M.
title Top-down approaches to abstract medical time series using linear segments
title_short Top-down approaches to abstract medical time series using linear segments
title_full Top-down approaches to abstract medical time series using linear segments
title_fullStr Top-down approaches to abstract medical time series using linear segments
title_full_unstemmed Top-down approaches to abstract medical time series using linear segments
title_sort top-down approaches to abstract medical time series using linear segments
publisher Institutional Knowledge at Singapore Management University
publishDate 2001
url https://ink.library.smu.edu.sg/sis_research/3046
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