Fundamentals of speech recognition
Fundamentals of speech recognition
Understanding nonlinear dynamics
Understanding nonlinear dynamics
Fuzzy sets as a basis for a theory of possibility
Fuzzy Sets and Systems
Classification of Mass Spectrometry Based Protein Markers by Kriging Error Matching
MDA '08 Proceedings of the 3rd international conference on Advances in Mass Data Analysis of Images and Signals in Medicine, Biotechnology, Chemistry and Food Industry
A time series representation model for accurate and fast similarity detection
Pattern Recognition
Entropies for detection of epilepsy in EEG
Computer Methods and Programs in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
GeoEntropy: A measure of complexity and similarity
Pattern Recognition
Computer Methods and Programs in Biomedicine
Weighted dynamic time warping for time series classification
Pattern Recognition
Possibilistic entropy: a new method for nonlinear dynamical analysis of biosignals
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part I
Classification trees for time series
Pattern Recognition
Approximate Entropy-Based Epileptic EEG Detection Using Artificial Neural Networks
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
Hi-index | 0.01 |
A nonlinear dynamical system can be defined as a study of any system that implies motion, change, or evolution in time where a change in one variable is not proportional to a change in a related variable. The mathematical operations underlying such a system are very useful for pattern recognition with time-series data. One of the most recent developments in nonlinear dynamical analysis is the so-called approximate entropy family. However, its algorithms are deterministic and do not consider uncertainty where the modeling of possibility can be appropriate and advantageous in many practical situations. Thus, possibilistic entropy algorithms are proposed in this paper as a new methodology for nonlinear dynamical analysis. The proposed approach is based on the notions of the approximate entropy family, geostatistics, and the theory of fuzzy sets. Furthermore, for the first time, nonlinear dynamical analysis of mass spectrometry data is presented for computer-based recognition of potential protein biomarkers and classification, which can be utilized for early disease prediction. Experimental results using proteomic and genetic data have shown the potential application of the proposed possibilistic nonlinear dynamical analysis to the study of complex biosignals.