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Paper details
Number 3 - September 2014
Volume 24 - 2014
Multiple neural network integration using a binary decision tree to improve the ECG signal recognition accuracy
Hoai Linh Tran, Van Nam Pham, Hoang Nam Vuong
Abstract
The paper presents a new system for ECG (ElectroCardioGraphy) signal recognition using different neural classifiers and a binary decision tree to provide one more processing stage to give the final recognition result. As the base classifiers, the three classical neural models, i.e., the MLP (Multi Layer Perceptron), modified TSK (Takagi–Sugeno–Kang) and the SVM (Support Vector Machine), will be applied. The coefficients in ECG signal decomposition using Hermite basis functions and the peak-to-peak periods of the ECG signals will be used as features for the classifiers. Numerical experiments will be performed for the recognition of different types of arrhythmia in the ECG signals taken from the MIT-BIH (Massachusetts Institute of Technology and Boston’s Beth Israel Hospital) Arrhythmia Database. The results will be compared with individual base classifiers’ performances and with other integration methods to show the high quality of the proposed solution.
Keywords
neural classifiers, integration of classifiers, decision tree, arrhythmia recognition, Hermite basis function decomposition