By Topic

Detecting nonlinear patterns in physiological signals

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Radhakrishnan, N. ; Dept. of Appl. Sci., Arkansas Univ., Little Rock, AR, USA ; Wilson, J.D. ; Hawk, Roger M.

The authors discuss a novel method to detect possible nonlinear structure in signals obtained from dynamical systems, which includes those obtained from physiological systems. The sampled discrete time series is first mapped onto a phase space by the method of delays. The vector series in phase space is partitioned into a finite number of clusters by the k-means technique. The determinant of the within-class scatter matrix provides an estimate of the hyper-ellipsoidal volume of the partitioned phase space. The objective is to look for significant differences in the hyper-ellipsoidal scatter volume between the original data and its corresponding surrogate realizations. The surrogate data sets were generated by the Iterated Amplitude Adjusted Fourier Transform technique (IAAFT). The null hypothesis addressed here is that the original data is a static nonlinear transform of a linearly correlated noise. The data sets analyzed include the uterine electromyography obtained during active labor

Published in:

Information Technology Applications in Biomedicine, 2000. Proceedings. 2000 IEEE EMBS International Conference on

Date of Conference:

2000