Possibilistic entropy: a new method for nonlinear dynamical analysis of biosignals

Authors:
Tuan D. Pham
Affiliations:
School of Engineeering and Information Technology, The University of New South Wales, Canberra, ACT, Australia
Venue:
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part I
Year:
2011

Citing 7
Cited 1

Understanding nonlinear dynamics

Understanding nonlinear dynamics
Fuzzy sets as a basis for a theory of possibility

Fuzzy Sets and Systems
Entropies for detection of epilepsy in EEG

Computer Methods and Programs in Biomedicine
GeoEntropy: A measure of complexity and similarity

Pattern Recognition
Entropy-based feature extraction and decision tree induction for breast cancer diagnosis with standardized thermograph images

Computer Methods and Programs in Biomedicine
Approximate Entropy-Based Epileptic EEG Detection Using Artificial Neural Networks

IEEE Transactions on Information Technology in Biomedicine
Computational Prediction Models for Early Detection of Risk of Cardiovascular Events Using Mass Spectrometry Data

IEEE Transactions on Information Technology in Biomedicine

Possibilistic nonlinear dynamical analysis for pattern recognition

Pattern Recognition

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Abstract

The theory of nonlinear dynamical systems has opened doors to discovering potential patterns hidden in complex time-series data. An attrative approach to nonlinear time-series analysis is the measure of predictability which characterizes the data in terms of entropy. A new entropy measure is presented in this paper as a new nonlinear dynamical method, which is based on the theory of possibility and the kriging computation. The proposed model has the potential for studying complex biosignals.