Uncovering non-linear structure in human ECG recordings

Michael Small, Dejin Yu, Jennifer Simonotto, Robert G. Harrison, Neil Grubb, K. A A Fox

Research output: Contribution to journalArticlepeer-review

50 Citations (Scopus)


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. © 2002 Elsevier Science Ltd. All rights reserved.

Original languageEnglish
Pages (from-to)1755-1762
Number of pages8
JournalChaos, Solitons and Fractals
Issue number8
Publication statusPublished - Aug 2002
Event29th Leeds-Lyon Symposium on Tribology - Leeds, United Kingdom
Duration: 3 Sept 20026 Sept 2002


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