Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/14794
Title: Uncovering non-linear structure in human ECG recordings
Authors: Small, M
Yu, D
Simonotto, J
Harrison, RG
Grubb, N
Fox, KAA
Issue Date: 2002
Source: Chaos, solitons and fractals, 2002, v. 13, no. 8, p. 1755-1762 How to cite?
Journal: Chaos, Solitons and Fractals 
Abstract: We employ surrogate data techniques and a new correlation dimension estimation algorithm, the Gaussian kernel algorithm, to uncover non-linearity in human electrocardiogram recordings during normal (sinus) rhythm, ventricular tachycardia (VT) and ventricular fibrillation (VF). We conclude that all three rhythms are not linear (i.e. distinct from a monotonic non-linear transformation of linearly filtered noise) and have significant correlations over a period greater than the inter-beat interval. Furthermore, we observe that sinus rhythm and VT exhibit a correlation dimension of approximately 2.3 and 2.4, respectively. The correlation dimension of VF exceeds 3.2. The entropy of sinus rhythm, VT and VF is approximately 0.69, 0.55, and 0.67 nats/s, respectively. These results indicate that techniques from non-linear dynamical systems theory should help us understand the mechanism underlying ventricular arrhythmia, and that these rhythms are likely to be a combination of low dimensional chaos and noise.
Description: Virtual Tissue Engineering of Cardiac Muscle(EPRSC), Leeds
URI: http://hdl.handle.net/10397/14794
ISSN: 0960-0779
DOI: 10.1016/S0960-0779(01)00168-0
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