R-wave detection using continuous wavelet modulus maxima

Modulus maxima derived from the continuous wavelet transform offers an enhanced time-frequency analysis technique for ECG signal analysis. Features within the ECG can be shown to correspond to various morphologies in the continuous modulus maxima domain. This domain has an easy interpretation and offers a good tool for the automatic characterization of the different components observed in the ECG in health and disease. As an application of these properties we have developed an R-wave detector and tested it using patient signals recorded in the Coronary Care Unit of the Royal Infirmary of Edinburgh (attaining a sensitivity of 99.53% and a positive predictive value of 99.73%) and with the MIT/BIH database (attaining a sensitivity of 99.7% and a positive predictive value of 99.68%).

1. Introduction

The surface electrocardiogram is a crucial diagnostic instrument in many areas of modern medicine. Analysis of the ECG has become an important area of research, in particular where advanced signal processing techniques can yield useful and timely information which is otherwise inaccessible, e.g. the interpretation of ventricular fibrillation during cardiac resuscitation. Traditionally, analytical tools extracting time-frequency information have been based around the Fourier Transform (for example [1], [2]). More recently, the continuous wavelet transform has been used successfully in the processing of ECG signals, and offers significant advantages - in particular the preservation of location specific features [3], [4], [5].