ABSTRACT
The detection of the P wave, QRS complex and T wave in an ECG is a problem still demanding the attention of those working in the field due to the time-varying morphology of the signal subject to physiological conditions and the presence of noise. Over the years, a number of wavelet-based techniques have been proposed to detect these features. Early work by Senhadji et al. (1995) compared the ability of wavelet transforms (based on three different wavelets: Daubechies, spline and Morlet) to recognize and describe isolated cardiac beats. Sahambi et al. (1997a,b) used a first-order derivative of the Gaussian function as a wavelet for the characterization of ECG waveforms. They applied modulus maxima-based wavelet analysis to detect and measure various parts of the signal, specifically the location of the onset and offset of the QRS complex and P and T waves. The group then undertook the same analysis for signals containing added baseline drift and high-frequency (HF) noise. They also computed intra-beat timing intervals to provide the relative positions of the components in the ECG which are important in delineating the electrical activity of the heart.
