Wavelet denoising as preprocessing stage to improve ICA performance in atrial fibrillation analysis

Authors:
César Sánchez;José Joaquín Rieta;Carlos Vayá;David Moratal Perez;Roberto Zangróniz;José Millet
Affiliations:
Innovation in Bioengineering, Castilla-La Mancha University, Cuenca, Spain;Bioengineering, Electronics and Telemedicine, Valencia University of Technology, Gandía, Spain;Bioengineering, Electronics and Telemedicine, Valencia University of Technology, Gandía, Spain;Bioengineering, Electronics and Telemedicine, Valencia University of Technology, Gandía, Spain;Innovation in Bioengineering, Castilla-La Mancha University, Cuenca, Spain;Bioengineering, Electronics and Telemedicine, Valencia University of Technology, Gandía, Spain
Venue:
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Year:
2006

Citing 4
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Abstract

Blind Source Separation (BSS) has been probed as one of the most effective techniques for atrial activity (AA) extraction in supraventricular tachyarrhythmia episodes like atrial fibrillation (AF). In these situations, a wavelet transform denoising stage can improve the extraction quality with low computational cost. Each ECG lead is processed to obtain its representation in the wavelet domain where the BSS systems improve their performance. The comparison of spectral parameters (main peak and power spectral density concentration) and statistics values (kurtosis) proves that the sparse decomposition in the wavelet domain of the observed mixtures reduces Gaussian contamination of these signals, speeds up the convergence and increase the quality of the extracted signal. The easy and fast implementation, robustness and efficiency are some of the main advantages of this technique making possible the application in real time systems as a support tool to clinical diagnostics.