Phase space embedding of electrocardiograms

Abstract

We study properties of the human electrocardiogram under the working hypothesis that fluctuations beyond the regular structure of single cardiac cycles are unpredictable. Against this background we discuss the possibility of using the phase space embedding method for this kind of signal. In particular, the specific nature of the stochastic or high-dimensional component allows us 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 that have not been previously applied in practice, and practical applications that have not yet been justified theoretically.