Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Performance Evaluation of Some Clustering Algorithms and Validity Indices
IEEE Transactions on Pattern Analysis and Machine Intelligence
Artificial Neural Network Based Automatic Cardiac Abnormalities Classification
ICCIMA '05 Proceedings of the Sixth International Conference on Computational Intelligence and Multimedia Applications
Advanced Methods And Tools for ECG Data Analysis
Advanced Methods And Tools for ECG Data Analysis
Expert Systems with Applications: An International Journal
Pattern Recognition Letters
A study on fuzzy C-means clustering-based systems in automatic spike detection
Computers in Biology and Medicine
Computers in Biology and Medicine
Integration of independent component analysis and neural networks for ECG beat classification
Expert Systems with Applications: An International Journal
A fuzzy clustering neural network architecture for classification of ECG arrhythmias
Computers in Biology and Medicine
Analysis of Myocardial Infarction Using Discrete Wavelet Transform
Journal of Medical Systems
Artificial neural networks for automatic ECG analysis
IEEE Transactions on Signal Processing
Adaptive probabilistic neural networks for pattern classification in time-varying environment
IEEE Transactions on Neural Networks
A patient-adaptive profiling scheme for ECG beat classification
IEEE Transactions on Information Technology in Biomedicine
Classification of Arrhythmia Using Hybrid Networks
Journal of Medical Systems
Automated cardiac event change detection for continuous remote patient monitoring devices
Proceedings of the 1st International Conference on Wireless Technologies for Humanitarian Relief
Expert Systems with Applications: An International Journal
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The role of electrocardiogram (ECG) as a noninvasive technique for detecting and diagnosing cardiac problems cannot be overemphasized. This paper introduces a fuzzy C-mean (FCM) clustered probabilistic neural network (PNN) for the discrimination of eight types of ECG beats. The performance has been compared with FCM clustered multi layered feed forward network (MLFFN) trained with back propagation algorithm. Important parameters are extracted from each ECG beat and feature reduction has been carried out using FCM clustering. The cluster centers form the input of neural network classifiers. The extensive analysis using the MIT-BIH arrhythmia database has shown an average classification accuracy of 97.54% with FCM clustered MLFFN and 99.58% with FCM clustered PNN. Fuzzy clustering improves the classification speed as well. The result reveals the capability of the FCM clustered PNN in the computer-aided diagnosis of ECG abnormalities.