Reliable atrial activity extraction from ECG atrial fibrillation signals

Authors:
Felipe Donoso;Eduardo Lecannelier;Esteban Pino;Alejandro Rojas
Affiliations:
Department of Electrical Engineering, University of Concepcion, Concepcion, Chile;Department of Internal Medicine, University of Concepcion, Concepcion, Chile;Department of Electrical Engineering, University of Concepcion, Concepcion, Chile;Department of Electrical Engineering, University of Concepcion, Concepcion, Chile
Venue:
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Year:
2011

Citing 2
Cited 1

Independent component analysis, a new concept?

Signal Processing - Special issue on higher order statistics
Independent component analysis: algorithms and applications

Neural Networks

Atrial activity selection for atrial fibrillation ECG recordings

Computers in Biology and Medicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

Atrial fibrillation (AF) is the most common arrhythmia encountered in clinical research, with a prevalence of 0.4% to 1% of the population. Therefore, the study of AF is an important research field that can provide great treatment improvements. In this paper we apply independent component analysis to a 12-lead electrocardiogram, for which we obtain a 12-source set. We apply to this set three different atrial activity (AA) selection methods based on: kurtosis, correlation of the sources with lead V1, and spectral analysis. We then propose a reliable AA extraction based on the consensus between the three methods in order to reduce the effect of anatomical and physiological variabilities. The extracted AA signal will be used in a future stage for AF classification.