PHASE SPACE EMBEDDING OF ELECTROCARDIOGRAMS

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.

[1]  Schreiber,et al.  Signal separation by nonlinear projections: The fetal electrocardiogram. , 1996, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[2]  E. Ott.  Chaos in Dynamical Systems: Contents , 1993 .

[3]  Thomas Schreiber,et al.  Processing of Physiological Data , 1998 .

[4]  D. T. Kaplan,et al.  Fetal ECG extraction with nonlinear state-space projections , 1998, IEEE Transactions on Biomedical Engineering.

[5]  D. T. Kaplan,et al.  Nonlinear noise reduction for electrocardiograms. , 1996, Chaos.

[6]  L. Glass,et al.  Understanding Nonlinear Dynamics , 1995 .

[7]  H. Kantz,et al.  Nonlinear time series analysis , 1997 .

[8]  P. Grassberger,et al.  On noise reduction methods for chaotic data. , 1993, Chaos.

[9]  Thomas Schreiber,et al.  Constrained Randomization of Time Series Data , 1998, chao-dyn/9909042.

[10]  J. Slocumb,et al.  A noninvasive method for recording the electrical activity of the human uterus in vivo. , 1994, Biomedical instrumentation & technology.

[11]  Mike E. Davies,et al.  Noise reduction by gradient descent , 1993 .

[12]  D. Broomhead,et al.  Takens embedding theorems for forced and stochastic systems , 1997 .

[13]  Schreiber,et al.  Noise reduction in chaotic time-series data: A survey of common methods. , 1993, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.