Hierarchical mixtures of experts and the EM algorithm
Neural Computation
A connectionist method for pattern classification with diverse features
Pattern Recognition Letters
ECG beats classification using multiclass support vector machines with error correcting output codes
Digital Signal Processing
Analysis of EEG signals by combining eigenvector methods and multiclass support vector machines
Computers in Biology and Medicine
Wavelet/mixture of experts network structure for EEG signals classification
Expert Systems with Applications: An International Journal
Adaptive mixtures of local experts
Neural Computation
Engineering Applications of Artificial Intelligence
The wavelet transform, time-frequency localization and signal analysis
IEEE Transactions on Information Theory
Classification of the electrocardiogram signals using supervised classifiers and efficient features
Computer Methods and Programs in Biomedicine
Fuzzy Hopfield neural network clustering for single-trial motor imagery EEG classification
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
In this paper, the usage of features in analysis of electrocardiographic changes in partial epileptic patients was presented. Two types of electrocardiogram (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. The classification accuracies of modified mixture of experts (MME), which were trained on diverse features, were obtained. The eigenvector methods (Pisarenko, multiple signal classification - MUSIC, and Minimum-Norm) were selected to generate the power spectral density (PSD) estimates. The features from the eigenvector PSD estimates, wavelet coefficients and Lyapunov exponents of the ECG signals were computed and statistical features were calculated to depict their distribution. The statistical features, which were used for obtaining the diverse features of the ECG signals, were then input into the implemented neural network models for training and testing purposes. The present study demonstrated that the MME trained on the diverse features achieved high accuracy rates (total classification accuracy of the MME is 99.44%).