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

Abstract

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.

Prioritization of e-learners activities using principal component analysis method

“…This approach used principle components of student learning attributes and classified the attributes independently using feed-forward neural network (NN) and least square-support vector machine (LS-SVM). Maik Pflugradt et al [20] applied Online learning algorithms for PCA on single-lead ECGs. Their model was evaluated by several adaptive approaches to solving the fundamental component analysis.…”

Young scientist award papers from the workshop on bioelectric and biomagnetic signal processing 2012

“…Pflugradt et al [ 6 ] provide a good example of the interplay between hardware and software. Their topic is on the online extraction of features from a single lead ECG using real-time PCA or alternatively single channel ICA.…”