Eigenvector methods for automated detection of electrocardiographic changes in partial epileptic patients

Authors:
Elif Derya Übeyli
Affiliations:
Department of Electrical and Electronics Engineering, TOBB Ekonomi ve Teknoloji U¨ niversitesi, Ankara, Turkey
Venue:
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
Year:
2009

Citing 11
Cited 2

Original Contribution: Stacked generalization

Neural Networks
Hierarchical mixtures of experts and the EM algorithm

Neural Computation
An ECG classifier designed using modified decision based neural networks

Computers and Biomedical Research
A connectionist method for pattern classification with diverse features

Pattern Recognition Letters
Improved learning algorithms for mixture of experts in multiclass classification

Neural Networks
A Mixture of Experts Network Structure Construction Algorithm for Modelling and Control

Applied Intelligence
Adaptive mixtures of local experts

Neural Computation
Feature extraction from Doppler ultrasound signals for automated diagnostic systems

Computers in Biology and Medicine
Detection of electrocardiographic changes in partial epileptic patients using Lyapunov exponents with multilayer perceptron neural networks

Engineering Applications of Artificial Intelligence
Feature saliency using signal-to-noise ratios in automated diagnostic systems developed for ECG beats

Expert Systems with Applications: An International Journal
Input feature selection for classification problems

IEEE Transactions on Neural Networks

Stability analysis of autonomous ratio-memory cellular nonlinear networks for pattern recognition

IEEE Transactions on Circuits and Systems Part I: Regular Papers
A patient-adaptive profiling scheme for ECG beat classification

IEEE Transactions on Information Technology in Biomedicine

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Abstract

In this paper, the automated diagnostic systems trained on diverse and composite features were presented for detection of electrocardiographic changes in partial epileptic patients. In practical applications of pattern recognition, there are often diverse features extracted from raw data that require recognizing. Methods of combining multiple classifiers with diverse features are viewed as a general problem in various application areas of pattern recognition. Two types (normal and partial epilepsy) of ECG beats (180 records from each class) were obtained from the Physiobank database. The multilayer perceptron neural network (MLPNN), 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 ECG signals, which were trained on diverse or composite features. Decision making was performed in two stages: feature extraction by eigenvector methods and classification using the classifiers trained on the extracted features. The present research demonstrated that the MME trained on the diverse features achieved accuracy rates (total classification accuracy is 99.44%) that were higher than that of the other automated diagnostic systems.