Paper Info
Title
Detection of Dynamical Transitions in Biomedical Signals Using Nonlinear Methods
Abstract
The ability to detect the existence of nonlinear dynamics may facilitate medical diagnostics for identifying, monitoring and predicting transitions from health to sickness. Detection of such transitions depends on the quality of the available biomedical signals and the relevance of the nonlinear statistics employed. The dangers of using irrelevant statistics are discussed. A simple intuitive nonlinear statistic, which evaluates changes in the distribution of points in state space is shown to be capable of detecting both linear and nonlinear dynamical transitions. This new technique, known as multi-dimensional probability evolution (MDPE), is illustrated using a synthetic signal obtained by mixing stochastic and a chaotic processes. Its utility in detecting transitions in biomedical data is demonstrated using a database of electrocardiograms collected from subjects who experienced partial epileptic seizures.
Year
DOI
Venue
2004
10.1007/978-3-540-30134-9_65
Lecture Notes in Artificial Intelligence
Keywords
Field
DocType
nonlinear dynamics,state space
Statistical physics,Nonlinear system,Statistic,Computer science,Nonlinear methods,Artificial intelligence,Medical diagnostics,Chaotic,State space,Lyapunov exponent,Machine learning
Conference
Volume
ISSN
Citations 
3215
0302-9743
1
PageRank 
References 
Authors
0.35
1
1
Name
Order
Citations
PageRank
Patrick E. Mcsharry113312.66