Authors:
C. Vayá
1
;
J. J. Rieta
1
and
R. Alcaraz
2
Affiliations:
1
Universidad Politécnica de Valencia, Spain
;
2
Universidad de Castilla-La Mancha, Spain
Keyword(s):
Atrial fibrillation, Blind source separation, Convolutive mixture, ECG, Wavelet transform.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Informatics in Control, Automation and Robotics
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Signal Processing, Sensors, Systems Modeling and Control
;
Time and Frequency Response
;
Time-Frequency Analysis
;
Wavelet Transform
Abstract:
In order to use the ECG as a tool for the characterization of atrial fibrillation (AF), we need to dissociate atrial activity (AA) from ventricular activity. On the other hand, the reduced number of leads recorded from a Holter system limits the necessary spatial diversity required by Blind Source Separation (BSS) techniques to accurately extract the AA. In this work, we propose a new method, the Convolutive Multiband Blind Separation (CMBS), to solve the problem of reduced number of leads by combining the Wavelet transform with the convolutive BSS algorithm Infomax. Our analysis shows up that CMBS improves the extraction performance of AA from Holter systems in comparison with previous extraction methods. This improvement is accomplished in two different scenarios, one for synthetic signals and another one for real signals. A high accuracy of the estimated AA for synthetic and real AF ECG episodes is reached in both scenarios. In addition, results prove that CMBS preserves the ori
ginal AA spectral parameters.
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