Online learning algorithms for principal component analysis applied on single-lead ECGs

This article evaluates several adaptive approaches to solve the principal component analysis (PCA) problem applied on single-lead ECGs. Recent studies have shown that the principal components can indicate morphologically or environmentally induced changes in the ECG signal and can be used to extract other vital information such as respiratory activity. Special interest is focused on the convergence behavior of the selected gradient algorithms, which is a major criterion for the usability of the gained results. As the right choice of learning rates is very data dependant and subject to movement artifacts, a new measurement system was designed, which uses acceleration data to improve the performance of the online algorithms. As the results of PCA seem very promising, we propose to apply a single-channel independent component analysis (SCICA) as a second step, which is investigated in this paper as well.