Feasibility and performance of methods based on statistical signal processing to study atrial fibrillation

In order to use the ECG as a tool for atrial fibrillation (AF) analysis, we need to separate the atrial activity (AA) from other cardioelectric signals. In this matter, some statistical signal processing techniques, such as Blind Source Separation (BSS), are able to perform a multi-lead statistical analysis of the ECG with the aim to obtain a set of independent sources where the AA is included. BSS techniques can be divided in two groups depending on the mixing model. Firstly, in algorithms based on Independent Component Analysis (ICA) instantaneous mixture of the sources is assumed. Secondly, in convolutive BSS (CBBS) algorithms the more realistic case of weighted and delayed contributions in the generation of the observed signals is considered. In this paper, a comparison between the performance of ICA algorithms and CBSS algorithms in the extraction of the AA in AF episodes is developed

[1]  V. Fuster,et al.  ACC/AHA/ESC Guidelines for the Management of Patients With Atrial Fibrillation: Executive Summary A Report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and the European Society of Cardiology Committee for Practice Guidelines and Policy Conference , 2001, Circulation.

[2]  D. Moratal-Perez,et al.  Bioelectric model of atrial fibrillation: applicability of blind source separation techniques for atrial activity estimation in atrial fibrillation episodes , 2003, Computers in Cardiology, 2003.

[3]  Juan Manuel Sánchis Rico.  Evaluación de las condiciones del sistema de mezcla en la separación ciega de fuentes de mezcla convolutiva para aplicación de audio , 2004 .

[4]  R. Lambert.  Multichannel blind deconvolution: FIR matrix algebra and separation of multipath mixtures , 1996 .

[5]  Shiro Ikeda,et al.  A Method of Blind Separation Based on Temporal Structure of Signals , 1998, ICONIP.

[6]  Pcw Piet Sommen,et al.  A new blind signal separation algorithm based on second-order statistics , 1998 .

[7]  D. B. Heppner,et al.  Considerations of quasi-stationarity in electrophysiological systems. , 1967, The Bulletin of mathematical biophysics.

[8]  Erkki Oja,et al.  Independent Component Analysis , 2001 .

[9]  李幼升,et al.  Ph , 1989 .

[10]  Nobuhiko Kitawaki,et al.  A combined approach of array processing and independent component analysis for blind separation of acoustic signals , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).