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Design of Microcomputer-Based Medical Instrumentation
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Ant Colony Optimization
ACODF: a novel data clustering approach for data mining in large databases
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Integration of independent component analysis and neural networks for ECG beat classification
Expert Systems with Applications: An International Journal
A new arrhythmia clustering technique based on Ant Colony Optimization
Journal of Biomedical Informatics
Application of ant K-means on clustering analysis
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Artificial neural networks for automatic ECG analysis
IEEE Transactions on Signal Processing
ECG analysis using nonlinear PCA neural networks for ischemiadetection
IEEE Transactions on Signal Processing
Ant system: optimization by a colony of cooperating agents
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An improved privacy-preserving DWT-based collaborative filtering scheme
Expert Systems with Applications: An International Journal
Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients
Computer Methods and Programs in Biomedicine
An effective ECG arrhythmia classification algorithm
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
International Journal of Mobile Learning and Organisation
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In this paper, Ant Colony Optimization (ACO) based clustering analysis of ECG arrhythmias taken from the MIT-BIH Arrhythmia Database is proposed. Both time domain and discrete wavelet transform (DWT) based frequency domain features are used in the analysis. Since the number of wavelet coefficients are huge amount as compared to the time domain parameters, Principal Component Analysis (PCA) based compression is applied on them in order to decrease their number to the number of time domain features. Then, the reduced numbers of frequency parameters are combined with the time domain features, in order to get the total feature sets. Different types of feature sets are tried and the classification results are compared. These are: time domain feature set, frequency domain feature set and the mixture of them. A neural network algorithm is developed in parallel to verify and measure the ACO classifier's success. Moreover, linear discriminant analysis (LDA) is used to show the effect of clustering on the system's results. The method is tested with MIT-BIH database to classify normal beats and five different critical and having vital importance arrhythmia types. Chosen six classes are normal sinus rhythm, premature ventricular contraction (PVC), atrial premature contraction (APC), right bundle branch block (RBBB), ventricular fusion (F) and fusion (f). Comparison results indicate that the mixture feature set gave a better success for the classification.