Wavelet Transform-Based Analysis of QRS complex in ECG Signals

TL;DR

This paper presents a wavelet transform method for analyzing ECG signals, focusing on efficiently detecting QRS complexes despite noise and artifacts, which can improve cardiac disorder diagnosis.

Contribution

It introduces a wavelet-based time-frequency analysis approach that effectively identifies QRS complexes across various ECG signal conditions, enhancing detection accuracy.

Findings

Wavelet coefficients accurately detect QRS complexes.

Method is robust against noise and baseline drift.

Potential for real-time ECG analysis on standard computers.

Abstract

In the present paper we have reported a wavelet based time-frequency multiresolution analysis of an ECG signal. The ECG (electrocardiogram), which records hearts electrical activity, is able to provide with useful information about the type of Cardiac disorders suffered by the patient depending upon the deviations from normal ECG signal pattern. We have plotted the coefficients of continuous wavelet transform using Morlet wavelet. We used different ECG signal available at MIT-BIH database and performed a comparative study. We demonstrated that the coefficient at a particular scale represents the presence of QRS signal very efficiently irrespective of the type or intensity of noise, presence of unusually high amplitude of peaks other than QRS peaks and Base line drift errors. We believe that the current studies can enlighten the path towards development of very lucid and time efficient algorithms for identifying and representing the QRS complexes that can be done with normal computers and processors.