We study properties of the human electrocardiogram under the working hypothesis that fluctuations beyond the regular structure of single cardiac cycles are unpredictable. On this background we discuss the possibility to use the phase space embedding method for this kind of signal. In particular, the specific nature of the stochastic or high dimensional component allows to use phase space embeddings for certain signal processing tasks. As practical applications, we discuss noise filtering, fetal ECG extraction, and the automatic detection of clinically relevant features. The main purpose of the paper is to connect results of embedding theory which had not been previously applied in practise, and practical applications which had not yet been justified theoretically.
@article{arxiv.chao-dyn/9807035,
title = {Phase space embedding of electrocardiograms},
author = {Marcus Richter and Thomas Schreiber},
journal= {arXiv preprint arXiv:chao-dyn/9807035},
year = {2009}
}