An automated ECG classification system using higher order spectra features and machine learning achieved an average accuracy of 97.65%, sensitivity of 98.75%, and specificity of 99.53%.
The electrocardiogram (ECG) is being commonly used as a diagnostic tool to distinguish different types of atrial tachyarrhythmias. The inherent complexity and mechanistic and clinical inter-relationships often brings about diagnostic difficulties to treating physicians and primary health care professionals creating frequent misdiagnoses and cross classifications using visual criteria. The current paper presents a methodology for ECG based pattern analysis for detection of atrial flutter, atrial fibrillation and normal sinus rhythm beats. ECG is an inherently non-linear and non-stationary signal; its variation may contain indicators of current disease, or warnings about impending cardiac diseases. Routinely used time domain and frequency domain methods will not be able to capture the hidden information present in the ECG beats. In the present study, we have used non-linear features of higher order spectra (HOS) to differentiate the normal, atrial fibrillation and atrial flutter ECG beats. The bispectrum features were subjected to independent component analysis (ICA) for data reduction. The ICA coefficients were subsequently subjected to K-nearest-neighbor (KNN), classification and regression tree (CART) and neural network (NN) classifiers to evaluate the best automated classifier. We have obtained an average accuracy of 97.65%, sensitivity and specificity of 98.75% and 99.53% respectively using ten-fold cross validation. Overall, the results show that application of higher order spectra statistics is useful for the classification of atrial tachyarrhythmias with reasonably high accuracies. Further validation of the proposed technique will yield acceptable results for clinical implementation.
Prasad et al. (Mon,) conducted a other in Atrial tachyarrhythmias (atrial fibrillation and atrial flutter). Higher order spectra (HOS) features with machine learning classifiers was evaluated on Classification accuracy for differentiating normal, atrial fibrillation, and atrial flutter ECG beats. An automated ECG classification system using higher order spectra features and machine learning achieved an average accuracy of 97.65%, sensitivity of 98.75%, and specificity of 99.53%.