Adaptive independent component analysis to analyze electrocardiograms
Seong-Bin Yim, Harold H Szu · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2001
In this work, we apply adaptive version independent component analysis (ADAPTIVE ICA) to the nonlinear measurement of electro-cardio-graphic (ECG) signals for potential detection of abnormal conditions in the heart. In principle, unsupervised ADAPTIVE ICA neural networks can demix the components of measured ECG signals. However, the nonlinear pre-amplification and post measurement processing make the linear ADAPTIVE ICA model no longer valid. This is possible because of a proposed adaptive rectification pre-processing is used to linearize the preamplifier of ECG, and then linear ADAPTIVE ICA is used in iterative manner until the outputs having their own stable Kurtosis. We call such a new approach adaptive ADAPTIVE ICA. Each component may correspond to individual heart function, either normal or abnormal. Adaptive ADAPTIVE ICA neural networks have the potential to make abnormal components more apparent, even when they are masked by normal components in the original measured signals. This is particularly important for diagnosis well in advance of the actual onset of heart attack, in which abnormalities in the original measured ECG signals may be difficult to detect. This is the first known work that applies Adaptive ADAPTIVE ICA to ECG signals beyond noise extraction, to the detection of abnormal heart function.