Application of Higher Order Cumulant Features for Cardiac Health Diagnosis Using Ecg Signals

Abstract

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.

An Efficient HOS-Based Gait Authentication of Accelerometer Data

“…Cumulants are statistical measures that exhibit some interesting properties [29], especially additivity and symmetry. Cumulant-based analysis of random signals was applied to a wide range of applications lately, especially in the field of biomedical signal processing [30], [31], [32] and image processing [33].…”

Computer-Aided Diagnosis of Depression Using EEG Signals

“…Even though a number of papers have been published using the nonlinear methods, there are other nonlinear methods [48,49,50,51,52,53,54,55,56,57,58,59,60,87,88,89,90,91,92,93,94,95,96,97,98] that are worth exploring for the EEG-based diagnosis of depression. As an example, figures 3a and b show sample bispectrum magnitude plots of EEG signals from the left brain hemisphere for normal and depression subjects shown in figure 2, respectively.…”

An efficient and automatic ECG arrhythmia diagnosis system using DWT and HOS features and entropy- based feature selection procedure

“…2 displays five categories of heartbeats that are denoised and segmented by the mentioned procedure. Feature extraction and selection ECG heartbeat classification and recognition depends on different features [26]. A dataset of features is constructed using the linear DWT-based and nonlinear HOS-based feature extraction techniques.…”