Authors:
Cuong C. To
and
Tuan D. Pham
Affiliation:
University of New South Wales, Australia
Keyword(s):
Entropy, Time series, Mass spectrometry, Genetic algorithms.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
Abstract:
Entropy methods including approximate entropy (ApEn), sample entropy (SampEn) and multiscale entropy (MSE) have recently been applied to measure the complexity of finite length time series for classification of diseases. In order to effectively use these entropy methods, parameters such as m, r, and scale factor (in MSE) are to be determined. So far, there have been no general rules to select these parameters as they depend on particular problems. In this paper, we introduce a genetic algorithm (GA) based method for optimal selection of these parameters in a sense that the entropic difference between healthy and pathologic groups are maximized.