Using Feature Construction to Improve the Performance of Neural Networks
Management Science
A fuzzy backpropagation algorithm
Fuzzy Sets and Systems
An ARMA order selection method with fuzzy reasoning
Signal Processing - Special section on information theoretic aspects of digital watermarking
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
Fuzzy least-squares algorithms for interactive fuzzy linear regression models
Fuzzy Sets and Systems - Theme: Modeling and learning
Evolutionary Training of a Neurofuzzy Network for Detection of P Wave of the ECG
ICCIMA '99 Proceedings of the 3rd International Conference on Computational Intelligence and Multimedia Applications
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
A fuzzy clustering neural network architecture for classification of ECG arrhythmias
Computers in Biology and Medicine
IEEE Transactions on Information Technology in Biomedicine
Compensatory neurofuzzy systems with fast learning algorithms
IEEE Transactions on Neural Networks
Conditional fuzzy clustering in the design of radial basis function neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Generalized clustering networks and Kohonen's self-organizing scheme
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Detection of valvular heart disorders using wavelet packet decomposition and support vector machine
Expert Systems with Applications: An International Journal
A new approach for epileptic seizure detection using adaptive neural network
Expert Systems with Applications: An International Journal
A novel approach for classification of ECG arrhythmias: Type-2 fuzzy clustering neural network
Expert Systems with Applications: An International Journal
EURASIP Journal on Advances in Signal Processing
Expert Systems with Applications: An International Journal
A comparative study of DWT, CWT and DCT transformations in ECG arrhythmias classification
Expert Systems with Applications: An International Journal
International Journal of Knowledge Engineering and Soft Data Paradigms
A fuzzy clustering neural networks for motion equations of synchro-drive robot
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Selection of wavelet packet measures for insufficiency murmur identification
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Heart beat classification using wavelet feature based on neural network
WSEAS TRANSACTIONS on SYSTEMS
A novel mobile epilepsy warning system
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
An improved procedure for detection of heart arrhythmias with novel pre-processing techniques
Expert Systems: The Journal of Knowledge Engineering
ECG arrhythmia classification based on optimum-path forest
Expert Systems with Applications: An International Journal
A methodology for embedded classification of heartbeats using random projections
Proceedings of the Conference on Design, Automation and Test in Europe
Neural network and wavelet average framing percentage energy for atrial fibrillation classification
Computer Methods and Programs in Biomedicine
International Journal of Mobile Learning and Organisation
Hi-index | 12.07 |
Principal component analysis (PCA) and wavelet transform (WT) are two powerful techniques for feature extraction. In addition, fuzzy c-means clustering (FCM) is among considerable techniques for data reduction. In other words, the aim of using FCM is to decrease the number of segments by grouping similar segments in training data. In this paper, four different structures, FCM-NN, PCA-NN, FCM-PCA-NN and WT-NN, are formed by using these two techniques and fuzzy c-means clustering. In addition, FCM-PCA-NN is the new method proposed in this paper for classification of ECG. This paper presents a comparative study of the classification accuracy of ECG signals by using these four structures for computationally efficient early diagnosis. Neural network used in this study is a well-known neural network architecture named as multi-layered perceptron (MLP) with backpropagation training algorithm. The ECG signals taken from MIT-BIH ECG database, are used in training to classify 10 different arrhythmias. These are normal sinus rhythm, sinus bradycardia, ventricular tachycardia, sinus arrhythmia, atrial premature contraction, paced beat, right bundle branch block, left bundle branch block, atrial fibrillation and atrial flutter. Before testing, the proposed structures are trained by backpropagation algorithm. All of the structures are tested by using experimental ECG records of 92 patients (40 male and 52 female, average age is 39.75+/-19.06). The test results suggest that FCM-PCA-NN structure can generalize better than PCA-NN and is faster than NN, FCM-NN and WT-NN.