NDLI: A Sequential Algorithm for Biological Event Detection Using Statistical Nonstationarity

Content Provider	IEEE Xplore Digital Library
Author	DiCecco, J. Salisbury, J. Ying Sun
Copyright Year	2006
Description	Author affiliation: Department of Electrical and Computer Engineering, University of Rhode Island, Kingston, RI 02881, USA; student in the Department of Electrical and Computer Engineering at the University of Rhode Island, Kingston, RI 02881, USA phone: 401-219-1021; e-mail: diceccoj@ele.uri.edu (DiCecco, J.)
Abstract	High dimension complex dynamical systems, such as those found in physiological processes, are often accompanied by nonstationarity. In many cases, the nonstationarity is caused by a physiologically significant event such as the prelude to ventricular fibrillation in cardiac arrest or the change of stasis by introducing pharmaceuticals. A need exists to be able to detect and monitor this change. Most conventional attempts at addressing this problem involve segmenting the time series and evaluating the statistics of the segments. The difficulty with this approach is that the nature of the nonstationarity can be transient, such that it is bounded by two, or more, regions of stationarity. Further, vacillation between stationary and nonstationary segments may continue for a significant portion of the time series. This paper will discuss the underlying statistical justification for asserting stationarity and the use of segmentation time series analysis techniques.
Starting Page	147
Ending Page	148
File Size	241046
Page Count	2
File Format	PDF
ISBN	0780395638
DOI	10.1109/NEBC.2006.1629795
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-04-01
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Event detection Biomedical monitoring Statistics Time series analysis Central nervous system Independent component analysis Statistical distributions Change detection algorithms Fibrillation Cardiac arrest nonstationarity Biomedical engineering electrophysiology neurophysiology nonlinearity stationarity
Content Type	Text
Resource Type	Article

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