A fast fixed-point algorithm for independent component analysis
Neural Computation
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Categorizing Heartbeats by Independent Component Analysis and Support Vector Machines
ISDA '08 Proceedings of the 2008 Eighth International Conference on Intelligent Systems Design and Applications - Volume 01
IEEE Transactions on Signal Processing
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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Electro Cardiogram (ECG) signals are affected by various kinds of noise and artifacts that may hide important information of interest. Independent component analysis is a new technique suitable for separating independent component from ECG complexes. This paper compares the various Independent Component Analysis (ICA) algorithms with respect to their capability to remove noise from ECG. The data bases of ECG samples attributing to different beat types were sampled from MIT-BIH arrhythmia database for experiment. We compare the signal to noise ratio (SNR) improvement in the real ECG data with different ICA algorithms also we compare the SNR for simulated ECG signal on matlab; giving the selection choice of various ICA algorithms for different database.