Key result
Radial Basis Function Network classifier using Empirical Mode Decomposition achieves ~96% accuracy for detecting arrhythmias.
A machine learning approach using a radial basis function network and empirical mode decomposition can accurately classify arrhythmias from ECG data.
May aid automated ECG arrhythmia detection; leaves open prospective clinical validation before practice change.
In this piece of work, the authors have attempted to classify four types of long duration arrhythmia electrocardiograms (ECG) using radial basis function network (RBFN). The data is taken from Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database, and features are extracted using empirical mode decomposition (EMD) technique. For most informative contents average power (AP) and coefficient of dispersion (CD) are evaluated from six intrinsic mode function (IMFs) of EMD. Principal component analysis (PCA) is used for feature reduction for effective classification using RBFN. The performance is shown in the result section, and it is found that the classification accuracy is 95.98%.
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Mohapatra et al. (2020) studied Arrhythmia (n=853). Radial Basis Function Network with Empirical Mode Decomposition and PCA vs. Other classifiers (SVM, ANN, PNN) was evaluated on Average classification accuracy. The proposed Radial Basis Function Network classifier using features from Empirical Mode Decomposition achieved an average classification accuracy of 95.98% for detecting arrhythmias.
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