Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0933-3657(02)00114-8
Title: Characterization of medical time series using fuzzy similarity-based fractal dimensions
Authors: Sarkar, M. 
Leong, T.-Y. 
Keywords: Box dimension
Characterization
Fractal
Fuzzy
Information dimension and correlation dimension
Time series
Issue Date: 2003
Source: Sarkar, M.,Leong, T.-Y. (2003). Characterization of medical time series using fuzzy similarity-based fractal dimensions. Artificial Intelligence in Medicine 27 (2) : 201-222. ScholarBank@NUS Repository. https://doi.org/10.1016/S0933-3657(02)00114-8
Abstract: This paper attempts to characterize medical time series using fractal dimensions. Existing fractal dimensions like box, information and correlation dimensions characterize the time series by measuring the rate at which the distribution of the time series changes when the length (or radius) of the box (or hypersphere) is changed. However, the measured dimensions significantly vary when the box (or hypersphere) position is changed slightly. It happens because the data points just outside the box (or hypersphere) are not accounted for, and all the data points inside the box or hypersphere are treated equally. To overcome these problems, the hypersphere is converted to a Gaussian, and thus the hard boundary becomes soft. The Gaussian represents the fuzzy similarity between the neighbors and the point around which the Gaussian is constructed. This concept of similarity is exploited to propose a fuzzy similarity-based fractal dimension. The proposed dimension aims to capture the regularity of the time series in terms of how the fuzzy similarity scales up/down when the resolution of the time series is decreased/increased. Experiments on intensive care unit (ICU) data sets show that the proposed dimension characterizes the time series better than the correlation dimension. © 2003 Elsevier Science B.V. All rights reserved.
Source Title: Artificial Intelligence in Medicine
URI: http://scholarbank.nus.edu.sg/handle/10635/39154
ISSN: 09333657
DOI: 10.1016/S0933-3657(02)00114-8
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

18
checked on Dec 5, 2017

WEB OF SCIENCETM
Citations

12
checked on Nov 2, 2017

Page view(s)

52
checked on Dec 9, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.