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Does a Spline Activated-Feed Forward Neural Network improve the classification accuracy of cardiac arrhythmias from ECG signals compared to a Radial Basis Function network?
Does a Spline Activated-Feed Forward Neural Network improve the classification accuracy of cardiac arrhythmias from ECG signals compared to a Radial Basis Function network?
A Spline Activated Feed Forward Neural Network slightly improves the automated classification accuracy of cardiac arrhythmias from ECG signals compared to a standard Radial Basis Function network.
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Modest accuracy gain in arrhythmia classification is hypothesis-generating; leaves open clinical ECG utility pending prospective validation.
Electro Cardiogram's (ECG) biomedical signals characterizing cardiac anomalies are used for identifying cardiac arrhythmia. Irregular heartbeat-Arrhythmia-affects heart rate causing problems. Many methods, trying to simplify arrhythmia monitoring through automated detection, were developed over the years. ECG classification for arrhythmia is investigated in this paper based on soft computing techniques. RR interval are extracted from time series of the ECG and used as feature for arrhythmia classification. Frequency domain extracted features are classified using Radial Basis Function (RBF) and proposed Spline Activated-Feed Forward Neural Network (SA-FFNN). Experiments were conducted with the Massachusetts Institute of Technology-Boston's Beth Israel Hospital (MIT-BIH) arrhythmia database for evaluating the proposed methods.
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R. Ganesh Kumar (2014) studied this question.
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