The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
ECG beats classification using multiclass support vector machines with error correcting output codes
Digital Signal Processing
Usage of eigenvector methods in implementation of automated diagnostic systems for ECG beats
Digital Signal Processing
Engineering Applications of Artificial Intelligence
A novel large-memory neural network as an aid in medical diagnosis applications
IEEE Transactions on Information Technology in Biomedicine
The wavelet transform, time-frequency localization and signal analysis
IEEE Transactions on Information Theory
Input feature selection for classification problems
IEEE Transactions on Neural Networks
Advances in automated diagnostic systems
NN'09 Proceedings of the 10th WSEAS international conference on Neural networks
Expert Systems with Applications: An International Journal
Hybrid model based on SVM with Gaussian loss function and adaptive Gaussian PSO
Engineering Applications of Artificial Intelligence
A comparative study of DWT, CWT and DCT transformations in ECG arrhythmias classification
Expert Systems with Applications: An International Journal
Characterizing an unknown pollution source in groundwater resources systems using PSVM and PNN
Expert Systems with Applications: An International Journal
Efficient forest fire occurrence prediction for developing countries using two weather parameters
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
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The aim of this study is to evaluate the diagnostic accuracy of the support vector machines (SVMs) on the electrocardiogram (ECG) signals. Two types of ECG beats (normal and partial epilepsy) were obtained from the MIT-BIH database. Post-ictal heart rate oscillations were studied in a heterogeneous group of patients with partial epilepsy. Decision making was performed in two stages: feature extraction by computing the wavelet coefficients and classification using the classifier trained on the extracted features. The purpose was to determine an optimum classification scheme for this problem, and also to infer clues about the extracted features. The present research demonstrated that the wavelet coefficients are the features, which well represent the ECG signals, and the SVMs trained on these features achieved high classification accuracies (total classification accuracy was 99.44%).