Complexity Information Based Analysis of Pathological ECG Rhythm for Ventricular Tachycardia and Ventricular Fibrillation

Based on surrogate data hypothesis testing method, this paper presents an improved nonlinear algorithm for analyzing deterministic chaotic signals, which can be applied to the analysis of abnormal rhythm electrocardiosignals. Ventricular tachycardia (VT) and ventricular fibrillation (VF) can be treated as nonlinear chaotic signals that are different from stochastic signals. On the basis of qualitative analysis theory in nonlinear dynamics, the authors forwarded a new definition of complexity rate and developed several relative detection methods for quantitative analysis of VT and VF. The results indicated that these qualitative analysis and quantitative analysis of VT and VF are objective and credible.

[1]  Chen Yushu,et al.  Threshold value for diagnosis of chaotic nature of the data obtained in nonlinear dynamic analysis , 1998 .

[2]  O. Tovar,et al.  The mechanism of defibrillation and cardioversion , 1996 .

[3]  M. Paluš,et al.  Detecting nonlinearity and phase synchronization with surrogate data , 1998, IEEE Engineering in Medicine and Biology Magazine.

[4]  N Radhakrishnan,et al.  Estimating regularity in epileptic seizure time-series data. A complexity-measure approach. , 1998, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[5]  B. Hao,et al.  Symbolic dynamics and characterization of complexity , 1991 .

[6]  F. Takens.  Detecting strange attractors in turbulence , 1981 .

[7]  Teodor Buchner,et al.  Symbolic dynamics and complexity in a physiological time series , 2000 .

[8]  J. Kurths,et al.  A Comparative Classification of Complexity Measures , 1994 .

[9]  James Theiler,et al.  Testing for nonlinearity in time series: the method of surrogate data , 1992 .

[10]  Anishchenko,et al.  Dynamical Entropies Applied to Stochastic Resonance , 1996, Physical review letters.

[11]  K. Narayanan,et al.  On the evidence of deterministic chaos in ECG: Surrogate and predictability analysis. , 1998, Chaos.

[12]  S. Pincus.  Approximate entropy (ApEn) as a complexity measure. , 1995, Chaos.

[13]  Nitish V. Thakor.  Chaos in the heart: signals and models , 1998, Proceedings of the 1998 2nd International Conference Biomedical Engineering Days.

[14]  Abraham Lempel,et al.  On the Complexity of Finite Sequences , 1976, IEEE Trans. Inf. Theory.

[15]  Arnold J. Mandell,et al.  Dynamical complexity and pathological order in the cardiac monitoring problem , 1987 .

[16]  Robert G. Harrison,et al.  Detecting dynamical nonstationarity in time series data. , 1999, Chaos.

[17]  Renzo Antolini,et al.  Complex dynamics underlying the human electrocardiogram , 1992, Biological Cybernetics.

[18]  Schreiber,et al.  Improved Surrogate Data for Nonlinearity Tests. , 1996, Physical review letters.

[19]  Xu-Sheng Zhang,et al.  Detecting ventricular tachycardia and fibrillation by complexity measure , 1999, IEEE Transactions on Biomedical Engineering.

[20]  P. Grassberger,et al.  Measuring the Strangeness of Strange Attractors , 1983 .

[21]  J. Kurths,et al.  Quantitative analysis of heart rate variability. , 1995, Chaos.

[22]  Alfonso M Albano,et al.  Phase-randomized surrogates can produce spurious identifications of non-random structure , 1994 .

[23]  Michael Small,et al.  Detecting Nonlinearity in Experimental Data , 1998 .

[24]  Theiler,et al.  Spurious dimension from correlation algorithms applied to limited time-series data. , 1986, Physical review. A, General physics.

[25]  P. Grassberger,et al.  Characterization of Strange Attractors , 1983 .

[26]  Michael Small,et al.  Deterministic nonlinearity in ventricular fibrillation. , 2000, Chaos.

[27]  Schuster,et al.  Easily calculable measure for the complexity of spatiotemporal patterns. , 1987, Physical review. A, General physics.