Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Wavelet applications in medicine
IEEE Spectrum
A connectionist method for pattern classification with diverse features
Pattern Recognition Letters
An improved neural classification network for the two-group problem
Computers and Operations Research
Adaptive mixtures of local experts
Neural Computation
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
Expert systems for time-varying biomedical signals using eigenvector methods
Expert Systems with Applications: An International Journal
Features extracted by eigenvector methods for detecting variability of EEG signals
Pattern Recognition Letters
Pattern Recognition Letters
Time-varying biomedical signals analysis with multiclass support vector machines
BIEN '07 Proceedings of the fifth IASTED International Conference: biomedical engineering
Computer Vision and Image Understanding
Decision support systems for time-varying biomedical signals: EEG signals classification
Expert Systems with Applications: An International Journal
EURASIP Journal on Advances in Signal Processing
Artificial Intelligence in Medicine
Implementation of automated diagnostic systems: ophthalmic arterial disorders detection case
International Journal of Systems Science
Expert Systems with Applications: An International Journal
Automatic recognition of epileptic seizure in EEG via support vector machine and dimension fractal
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Selection of effective features for ECG beat recognition based on nonlinear correlations
Artificial Intelligence in Medicine
Advances in Engineering Software
Application of mixture of experts to construct real estate appraisal models
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Detection and Localization of Myocardial Infarction using K-nearest Neighbor Classifier
Journal of Medical Systems
Expert Systems with Applications: An International Journal
Sparse Representation-Based Heartbeat Classification Using Independent Component Analysis
Journal of Medical Systems
Investigation of mixture of experts applied to residential premises valuation
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part II
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This paper illustrates the use of modified mixture of experts (MME) network structure to guide model selection for classification of electrocardiogram (ECG) beats with diverse features. The MME is a modular neural network architecture for supervised learning. Expectation-maximization (EM) algorithm was used for training the MME so that the learning process is decoupled in a manner that fits well with the modular structure. The 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 MME network structure for training and testing purposes. We explored the ability of designed and trained MME network structure, combined with wavelet preprocessing (computing wavelet coefficients) and nonlinear dynamics tools (computing Lyapunov exponents), to discriminate five types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat, partial epilepsy beat) obtained from the Physiobank database. The MME achieved accuracy rates which were higher than that of the mixture of experts (ME) and feedforward neural network models (multilayer perceptron neural network-MLPNN). The proposed MME approach can be useful in classifying long-term ECG signals for early detection of heart diseases/abnormalities.