Original Contribution: Stacked generalization
Neural Networks
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
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
Feature extraction from Doppler ultrasound signals for automated diagnostic systems
Computers in Biology and Medicine
A modified mixture of experts network structure for ECG beats classification with diverse features
Engineering Applications of Artificial Intelligence
Recurrent neural networks employing Lyapunov exponents for EEG signals classification
Expert Systems with Applications: An International Journal
A novel large-memory neural network as an aid in medical diagnosis applications
IEEE Transactions on Information Technology in Biomedicine
Input feature selection for classification problems
IEEE Transactions on Neural Networks
Statistics over features of ECG signals
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
In this paper, we present the expert systems for time-varying biomedical signals classification and determine their accuracies. The combined neural network (CNN), mixture of experts (ME), and modified mixture of experts (MME) were tested and benchmarked for their performance on the classification of the studied time-varying biomedical signals (ophthalmic arterial Doppler signals, internal carotid arterial Doppler signals and electroencephalogram signals). Decision making was performed in two stages: feature extraction by eigenvector methods and classification using the classifiers trained on the extracted features. The inputs of these expert systems composed of diverse or composite features were chosen according to the network structures. The present study was conducted with the purpose of answering the question of whether the expert system with diverse features (MME) or composite feature (CNN, ME) improve the capability of classification of the time-varying biomedical signals. The purpose was to determine an optimum classification scheme for the problem and also to infer clues about the extracted features. Our research demonstrated that the power levels of power spectral density (PSD) estimations obtained by the eigenvector methods are the valuable features which are representing the time-varying biomedical signals and the CNN, ME, and MME trained on these features achieved high classification accuracies.