Int J Neural Syst - Application of higher order cumulant features for cardiac health diagnosis using ECG signals.

Tópicos

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Resumo

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.

Resumo Limpo

electrocardiogram ecg electr activ heart indic p qrs t wave minut chang amplitud durat ecg depict particular type cardiac abnorm difficult deciph hidden inform present nonlinear nonstationari signal automat diagnost system character cardiac activ ecg signal provid insight phenomena therebi reveal import clinic inform various method propos detect cardiac abnorm ecg record applic higher order spectra hos featur seem promis approach can captur nonlinear dynam natur ecg signal paper automat classifi five type beat use hos featur higher order cumul use two differ approach five type ecg beat normal n right bundl branch block rbbb left bundl branch block lbbb atrial prematur contract apc ventricular prematur contract vpc first approach cumul featur segment ecg signal use classif wherea second approach cumul discret wavelet transform dwt coeffici use featur classifi approach cumul featur subject data reduct use princip compon analysi pca classifi use three layer feedforward neural network nn least squaresupport vector machin lssvm classifi studi obtain highest averag accuraci sensit specif use first approach nn classifi develop system readi clinic run larg dataset

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