Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
An Online Algorithm for Segmenting Time Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Temporal granulation and its application to signal analysis
Information Sciences—Informatics and Computer Science: An International Journal
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Knowledge-Based Clustering: From Data to Information Granules
Knowledge-Based Clustering: From Data to Information Granules
On fuzzy cluster validity indices
Fuzzy Sets and Systems
ECG signal compression and classification algorithm with quad level vector for ECG holter system
IEEE Transactions on Information Technology in Biomedicine
A patient-adaptive profiling scheme for ECG beat classification
IEEE Transactions on Information Technology in Biomedicine
Active learning methods for electrocardiographic signal classification
IEEE Transactions on Information Technology in Biomedicine
Genetic design of feature spaces for pattern classifiers
Artificial Intelligence in Medicine
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Signal Scaling by Centered Discrete Dilated Hermite Functions
IEEE Transactions on Signal Processing
Efficient Compression of QRS Complexes Using Hermite Expansion
IEEE Transactions on Signal Processing
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Objective: The study introduces and elaborates on a certain perspective of biomedical data analysis where data structure is revealed through fuzzy clustering. The key objective of the study is to develop a characterization of the content of the clusters by offering a number of their descriptors established on the basis of membership grades of patterns included there, as well as on the basis of their class membership. Next, a design of a cluster-based classifier is presented in which the structure of the classifier is based on a collection of clusters. The structure also exploits the descriptors of the clusters as well as aggregates their characteristics with the activation levels of the associated clusters formed in the feature space in which QRS complexes are represented. Methods and materials: The underlying methods involve the use of fuzzy clustering and two essential ways of representing QRS complexes with the use of the Hermite expansion of signals and piecewise aggregate approximation (PAA). The material involves QRS segments coming from the MIT-BIH Arrhythmia Database. Results: The key results demonstrate and quantify the effectiveness of QRS characterization with the use of clustering realized in the space of coefficients of the Hermite series expansion and the PAA expansion. In general, accuracy of the discussed classification schemes increases with the increase of the number of clusters; the difference varies in the range of 30% (when moving from 10 to 60 clusters). The fuzzification coefficient of the fuzzy C-Means clustering algorithm has a visible impact on the quality of the results in the range of up 40% difference in the classification of accuracy (when the coefficient varies in-between 1.1 and 2.5). The PAA representation space leads to slightly better results than those obtained when using the Hermite representation of the signals, the difference is of around 5%. Conclusions: It was shown that granular representation of electrocardiographic signals is essential to data analysis and classification by providing a means to reveal and characterize the data structure and by providing prerequisites to construct pattern classifiers. The study also shows that fuzzy clusters deliver important structural information about the data that could be further quantified by looking into the content of clusters.