Uncertainty, fuzzy logic, and signal processing
Signal Processing - Special issue on fuzzy logic in signal processing
Centroid of a type-2 fuzzy set
Information Sciences: an International Journal
Unsupervised feature extraction using neuro-fuzzy approach
Fuzzy Sets and Systems - Information processing
A fuzzy neural network for pattern classification and feature selection
Fuzzy Sets and Systems
Suppressed fuzzy c-means clustering algorithm
Pattern Recognition Letters
A fuzzy c-means variant for the generation of fuzzy term sets
Fuzzy Sets and Systems - Theme: Modeling and learning
A Self-Organizing Neural Fuzzy Inference Network
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5 - Volume 5
Expert Systems with Applications: An International Journal
Pattern Recognition Letters
Usage of eigenvector methods in implementation of automated diagnostic systems for ECG beats
Digital Signal Processing
Integration of independent component analysis and neural networks for ECG beat classification
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Computer Methods and Programs in Biomedicine
A fuzzy clustering neural network architecture for classification of ECG arrhythmias
Computers in Biology and Medicine
Uncertain Fuzzy Clustering: Insights and Recommendations
IEEE Computational Intelligence Magazine
IEEE Transactions on Fuzzy Systems
ECG arrhythmia classification based on optimum-path forest
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
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This paper presents a new automated diagnostic system to classification of electrocardiogram (ECG) arrhythmias. The diagnostic system is executed using type-2 fuzzy c-means clustering (T2FCM) algorithm, wavelet transform (WT) and neural network. Method of combining T2FCM and WT is used to improve performance of neural network. We aimed high accuracy rate to classification of ECG beats and constituted the automated diagnostic system to improve of classifier's performance. Ten types of ECG beats selected from MIT-BIH database were used to train the system. Then, this system was tested by the ECG signals of patients. The classification accuracy of the proposed classifier, type-2 fuzzy clustering wavelet neural network (T2FCWNN), is compared with the structures formed by type-1 FCM and WT. Process of T2FCWNN architecture is realized on three stages. First stage is formed the new training set obtained by selection of the best segments for each arrhythmia class using T2FCM. Second stage is feature extraction by WT on the new training set. Third stage is classification of the extracted features using neural network. The research showed that accuracy rate was found as 99% using this system.