Cumulant based Teager energy operator for ECG signal modeling

In this paper a cumulant based Teager energy operator (TEO) method is proposed to enhance the morphological features of noisy stress ECG signals which are corrupted with Gaussian noise, and other artifacts like baseline wander. It uses higher order statistical (HOS) cumulants in combination with nonlinear TEO required for the analysis of nonlinear and non stationary signals. Higher order cumulants suppress the Gaussian noise and TEO derives an energy from nonlinear components in time and frequency domains. It exhibits an attractive feature of retrieving a clean ECG from noisy recordings with extremely poor SNR of up to 10dB. The mean of Teager energy (TE) in time as well as frequency domains and improvement in signal to noise ratio (SNRI) are the statistical measures used for classification. Results reveal the efficacy of the proposed method in improving the signal to noise ratio and computing the energy in time and frequency domains.