Analysis of surface electrocardiograms in atrial fibrillation: techniques, research, and clinical applications

Atrial fibrillation (AF) is the most common arrhythmia encountered in clinical practice. Neither the natural history of AF nor its response to therapy is sufficiently predictable by clinical and echocardiographic parameters. The purpose of this article is to describe technical aspects of novel electrocardiogram (ECG) analysis techniques and to present research and clinical applications of these methods for characterization of both the fibrillatory process and the ventricular response during AF. Atrial fibrillatory frequency (or rate) can reliably be assessed from the surface ECG using digital signal processing (extraction of atrial signals and spectral analysis). This measurement shows large inter-individual variability and correlates well with intra-atrial cycle length, a parameter which appears to have primary importance in AF maintenance and response to therapy. AF with a low fibrillatory rate is more likely to terminate spontaneously and responds better to antiarrhythmic drugs or cardioversion, whereas high-rate AF is more often persistent and refractory to therapy. Ventricular responses during AF can be characterized by a variety of methods, which include analysis of heart rate variability, RR-interval histograms, Lorenz plots, and non-linear dynamics. These methods have all shown a certain degree of usefulness, either in scientific explorations of atrioventricular (AV) nodal function or in selected clinical questions such as predicting response to drugs, cardioversion, or AV nodal modification. The role of the autonomic nervous system for AF sustenance and termination, as well as for ventricular rate responses, can be explored by different ECG analysis methods. In conclusion, non-invasive characterization of atrial fibrillatory activity and ventricular response can be performed from the surface ECG in AF patients. Different signal processing techniques have been suggested for identification of underlying AF pathomechanisms and prediction of therapy efficacy.