A feature extraction method combining bispectrum and 2D graph Fourier transform with an SVM-RBF classifier achieved a high classification accuracy of 96.2% for ECG arrhythmia detection.
A novel feature extraction method combining bispectrum and 2D graph Fourier transform achieved 96.2% accuracy in classifying ECG arrhythmias.
Heart diseases are in the front rank among several kinds of life threats, due to its high incidence and mortality. Regarded as a powerful tool in the diagnosis of the cardiac disorder and arrhythmia detection, analysis of electrocardiogram (ECG) signals has become the focus of numerous researches. In this study, a feature extraction method based on the bispectrum and 2D graph Fourier transform (GFT) was developed. High-order matrix founded on bispectrum are extended into structured datasets and transformed into the eigenvalue spectrum domain by GFT, so that features can be extracted from statistical quantities of eigenvalues. Spectral features have been computed to construct the feature vector. Support vector machine based on the radial basis function kernel (SVM-RBF) was used to classify different arrhythmia heartbeats downloaded from the Massachusetts Institute of Technology - Beth Israel Hospital (MIT-BIH) Arrhythmia Database, according to the Association for the Advancement of Medical Instrumentation (AAMI) standard. Based on the cross-validation method, the experimental results depicted that our proposed model, the combination of bispectrum and 2D-GFT, achieved a high classification accuracy of 96.2%.
Liu et al. (Thu,) conducted a other in Arrhythmia. Feature extraction method based on bispectrum and 2D graph Fourier transform (GFT) with SVM-RBF classifier was evaluated on Classification accuracy. A feature extraction method combining bispectrum and 2D graph Fourier transform with an SVM-RBF classifier achieved a high classification accuracy of 96.2% for ECG arrhythmia detection.