The principles of software QRS detection

TLDR

The QRS complex, the most prominent ECG waveform, is essential for heart rate determination, cardiac cycle classification, and ECG compression, and its detection has been a 30‑year research focus that has evolved alongside advances in computer technology and new algorithmic approaches such as neural networks, genetic algorithms, wavelet transforms, filter banks, and heuristic nonlinear transforms. The authors aim to review recent and earlier QRS detection algorithms. They provide an overview of these algorithms.

Abstract

The QRS complex is the most striking waveform within the electrocardiogram (ECG). Since it reflects the electrical activity within the heart during the ventricular contraction, the time of its occurrence as well as its shape provide much information about the current state of the heart. Due to its characteristic shape it serves as the basis for the automated determination of the heart rate, as an entry point for classification schemes of the cardiac cycle, and often it is also used in ECG data compression algorithms. In that sense, QRS detection provides the fundamentals for almost all automated ECG analysis algorithms. Software QRS detection has been a research topic for more than 30 years. The evolution of these algorithms clearly reflects the great advances in computer technology. Within the last decade many new approaches to QRS detection have been proposed; for example, algorithms from the field of artificial neural networks genetic algorithms wavelet transforms, filter banks as well as heuristic methods mostly based on nonlinear transforms. The authors provide an overview of these recent developments as well as of formerly proposed algorithms.