Identification of Premature Ventricular Cycles of Electrocardiogram Using Discrete Cosine Transform-Teager Energy Operator Model.

Vallem Sharmila,K Ashoka Reddy

doi:10.1155/2015/438569

Abstract

An algorithm based on the ability of TEO to track the changes in the envelope of ECG signal is proposed for identifying PVCs in ECG. Teager energy is calculated from DCT coefficients of ECG signal. This method can be considered as computationally efficient algorithm when compared with the well-known DCT cepstrum technique. EPE is derived from the teager energy of DCT coefficients in DCT-TEO method and from the cepstrum of DCT coefficients in the existing method. EPE determines the decay rate of the action potential of ECG beat and provides sufficient information to identify the PVC beats in ECG data. EPEs obtained by DCT-TEO and existing DCT cepstrum models are compared. The proposed algorithm has resulted in performance measures like sensitivity of 98–100%, positive predictivity of 100%, and detection error rate of 0.03%, when tested on MIT-BIH database signals consisting of PVC and normal beats. Result analysis reveals that the DCT-TEO algorithm worked well in clear identification of PVCs from normal beats compared to the existing algorithm, even in the presence of artifacts like baseline wander, PLI, and noise with SNR of up to −5 dB.

Highlights

SA node fires electrical impulses at regular intervals that travel through the conduction pathways of cardiac musculature [1]
We found the proposed DCTTEO algorithm as a simple technique that gives remarkable results in distinguishing the PVC beat from normal beat when compared to the well known Discrete cosine transform (DCT)-cepstrum algorithm [9]
Envelopes extracted from DCT coefficients using nonlinear Teager energy operator for a PVC beat and normal beat with the proposed method are shown in Figures 2 and 3

Summary

Introduction

SA node fires electrical impulses at regular intervals that travel through the conduction pathways of cardiac musculature [1] These excitation impulses allow the contraction and expansion of cardiac muscles which when recorded give ECG consisting of three distinct features referred to as P, QRS, and T waves. Kaiser [7] studied the basic concepts of TEO and found that TEO can be used for energy extraction from nonlinear signals and achieved successful classification of arrhythmia beats from normal beats. A DCT-TEO modeling based algorithm is proposed to extract the energy of ECG beat for identifying PVCs. Many linear energy operators are available in the literature to extract the energy of linear signals that give energy proportional to the square of the amplitude of the signal.

Methods

Results

Conclusions