Higher order cumulant features of segmented ECG signals classified using a neural network achieved an average accuracy of 94.52%, sensitivity of 98.61%, and specificity of 98.41% for beat diagnosis.
An automated system using higher order cumulant features and neural network classifiers can accurately classify five types of ECG beats, potentially aiding in cardiac health diagnosis.
Electrocardiogram (ECG) is the electrical activity of the heart indicated by P, Q-R-S and T wave. The minute changes in the amplitude and duration of ECG depicts a particular type of cardiac abnormality. It is very difficult to decipher the hidden information present in this nonlinear and nonstationary signal. An automatic diagnostic system that characterizes cardiac activities in ECG signals would provide more insight into these phenomena thereby revealing important clinical information. Various methods have been proposed to detect cardiac abnormalities in ECG recordings. Application of higher order spectra (HOS) features is a seemingly promising approach because it can capture the nonlinear and dynamic nature of the ECG signals. In this paper, we have automatically classified five types of beats using HOS features (higher order cumulants) using two different approaches. The five types of ECG beats are normal (N), right bundle branch block (RBBB), left bundle branch block (LBBB), atrial premature contraction (APC) and ventricular premature contraction (VPC). In the first approach, cumulant features of segmented ECG signal were used for classification; whereas in the second approach cumulants of discrete wavelet transform (DWT) coefficients were used as features for classifiers. In both approaches, the cumulant features were subjected to data reduction using principal component analysis (PCA) and classified using three layer feed-forward neural network (NN) and least square-support vector machine (LS-SVM) classifiers. In this study, we obtained the highest average accuracy of 94.52%, sensitivity of 98.61% and specificity of 98.41% using first approach with NN classifier. The developed system is ready clinically to run on large datasets.
Martis et al. (Mon,) conducted a other in Cardiac abnormalities (N, RBBB, LBBB, APC, VPC). Higher order cumulant features of segmented ECG signals with neural network classifier vs. Cumulants of discrete wavelet transform coefficients was evaluated on Classification accuracy, sensitivity, and specificity for five types of ECG beats. Higher order cumulant features of segmented ECG signals classified using a neural network achieved an average accuracy of 94.52%, sensitivity of 98.61%, and specificity of 98.41% for beat diagnosis.
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