Key result
Integrating multiple neural classifiers using a binary decision tree achieved a testing error of 1.24% for ECG arrhythmia recognition, outperforming the best individual classifier (SVM, 1.96%).
Why the study?
Does multiple neural network integration using a binary decision tree improve ECG signal recognition accuracy for arrhythmias compared to individual classifiers?
Does multiple neural network integration using a binary decision tree improve ECG signal recognition accuracy for arrhythmias compared to individual classifiers?
Absolute Event Rate: 1.24% vs 1.96%
Integrating multiple neural networks with a binary decision tree improves the accuracy of automated ECG arrhythmia recognition.
Should not yet change clinical ECG interpretation; hypothesis-generating for ensemble neural methods in arrhythmia classification.
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
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Tran et al. (2014) studied Arrhythmia (n=19). Multiple neural network integration using a binary decision tree vs. Individual base classifiers (SVM, TSK, MLP) and other integration methods was evaluated on Testing error rate for ECG signal recognition. Integrating multiple neural classifiers using a binary decision tree achieved a testing error of 1.24% for ECG arrhythmia recognition, outperforming the best individual classifier (SVM, 1.96%).
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