Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory
Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory
ECG beat classification using neuro-fuzzy network
Pattern Recognition Letters
Expert Systems with Applications: An International Journal
A switchable scheme for ECG beat classification based on independent component analysis
Expert Systems with Applications: An International Journal
ECG beats classification using multiclass support vector machines with error correcting output codes
Digital Signal Processing
Pattern Recognition Letters
Computer aided diagnosis of ECG data on the least square support vector machine
Digital Signal Processing
Artificial Intelligence in Medicine
Support vector machines for detection of electrocardiographic changes in partial epileptic patients
Engineering Applications of Artificial Intelligence
Selection of significant independent components for ECG beat classification
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
A fuzzy clustering neural network architecture for classification of ECG arrhythmias
Computers in Biology and Medicine
A modified mixture of experts network structure for ECG beats classification with diverse features
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
An effective ECG arrhythmia classification algorithm
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Detection of Carotid Artery Disease by Using Learning Vector Quantization Neural Network
Journal of Medical Systems
ECG arrhythmia classification based on optimum-path forest
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
In this paper, a novel use of Kernel-Adatron (K-A) learning algorithm to aid SVM (Support Vector Machine) for ECG arrhythmias classification is proposed. The proposed pattern classifier is compared with MLP (multi-layered perceptron) using back propagation (BP) learning algorithm. The ECG signals taken from MIT-BIH arrhythmia database are used in training to classify 6 different arrhythmia, plus normal ECG. The MLP and SVM training and testing stages were carried out twice. They were first trained only with one ECG lead signal and then a second ECG lead signal was added to the training and testing datasets. The aim was to investigate its influence on training and testing performance (generalization ability) plus time of training for both classifiers. Implementation of these three criteria for evaluation of ECG signals classification will ease the problem of structural comparisons, which has not been given attention in previous research works. The results indicate that SVM in comparison to MLP is much faster in training stage and nearly seven times higher in performance, but MLP generalization ability in terms of mean square error is more than three times less. The proposed SVM method shows considerable improvement in comparison to recently reported results obtained by Osowski et al. (2008).