Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Atrial Activity Extraction Based on Statistical and Spectral Features
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Artificial Intelligence in Medicine
Extraction of atrial activity from the ECG by spectrally constrained ICA based on kurtosis sign
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Semi-blind source extraction of atrial activity by combining statistical and spectral features
Digital Signal Processing
Separation of periodically time-varying mixtures using second-order statistics
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
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A novel non-invasive approach for the atrial wave estimation in atrial fibrillation (AF) episodes is presented. The method is based on the maximum likelihood (ML) solution of independent component analysis (ICA). The densities of the bioelectric independent sources corresponding to the ventricular and atrial activities are previously analyzed, and the prior knowledge extracted from them is considered in order to develop an appropriate separation model. As a consequence, the sources can be recovered through the optimization of the ML criterion. The algorithm is validated using a significant database of synthesized and real AF electrocardiograms (ECGs). A simulation model for the generation of realistic AF signals with known AA is also defined. The results show good performance in terms of SNR and correlation indices when estimated AA is compared to known AA. Regarding the real AF ECGs, the AA sources could always be estimated. Successful AA extraction was validated using spectral parameters. The main frequency of the atrial wave ranged from 4.9 to 7.4 Hz, and the spectral concentration was 49.8% on average.