Fuzzy mathematical techniques with applications
Fuzzy mathematical techniques with applications
An attribute grammar for QRS detection
Pattern Recognition
Syntactic ECG processing: a review
Pattern Recognition
Syntactic Pattern Recognition of the ECG
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
A syntactic algorithm for peak detection in waveforms with applications to cardiography
Communications of the ACM
Fuzzy equalization in the construction of fuzzy sets
Fuzzy Sets and Systems
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Fuzzy logic = computing with words
IEEE Transactions on Fuzzy Systems
Temporal analysis of clusters of supermarket customers: conventional versus interval set approach
Information Sciences—Informatics and Computer Science: An International Journal
A fast algorithm for one-unit ICA-R
Information Sciences: an International Journal
Evaluation of laser dynamic speckle signals applying granular computing
Signal Processing
Temporal analysis of clusters of supermarket customers: conventional versus interval set approach
Information Sciences: an International Journal
Granulation-based symbolic representation of time series and semi-supervised classification
Computers & Mathematics with Applications
Fuzzy numbers from raw discrete data using linear regression
Information Sciences: an International Journal
Data structure-guided development of electrocardiographic signal characterization and classification
Artificial Intelligence in Medicine
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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In this study, we elaborate on the role of information granulation and the ensuing information granules in description of time series and signal analysis, in general. Information granules are entities of elements (quite commonly, numeric data) that are combined together (aggregated) owing to their vicinity, similarity and alike. Proceeding with a given window of granulation (that is an initial collection of numeric data), we propose an algorithm that produces a complete information granule - fuzzy set. The principle supported by the method leads to the formation of fuzzy sets that are legitimate in terms of experimental data being at the same time maximized with regard to their specificity (compactness). It has been shown that information granules can be are regarded as generic conceptual entities contributing to the description of numeric time series. In this capacity, they are used as building blocks aimed at achieving high level, compact, and comprehensible models of signals. More importantly, the phase of information granulation could be viewed as a prerequisite to more synthetic and abstract processing such as the one witnessed in syntactic pattern recognition.