Using ordinal partition transition networks to analyze ECG data.

Electrocardiogram (ECG) data from patients with a variety of heart conditions are studied using ordinal pattern partition networks. The ordinal pattern partition networks are formed from the ECG time series by symbolizing the data into ordinal patterns. The ordinal patterns form the nodes of the network and edges are defined through the time ordering of the ordinal patterns in the symbolized time series. A network measure, called the mean degree, is computed from each time series-generated network. In addition, the entropy and number of non-occurring ordinal patterns (NFP) is computed for each series. The distribution of mean degrees, entropies, and NFPs for each heart condition studied is compared. A statistically significant difference between healthy patients and several groups of unhealthy patients with varying heart conditions is found for the distributions of the mean degrees, unlike for any of the distributions of the entropies or NFPs.

[1]  G.B. Moody,et al.  The impact of the MIT-BIH Arrhythmia Database , 2001, IEEE Engineering in Medicine and Biology Magazine.

[2]  A. Tamhane.  Multiple comparisons in model i one-way anova with unequal variances , 1977 .

[3]  S. Shapiro,et al.  An Analysis of Variance Test for Normality (Complete Samples) , 1965 .

[4]  Lucas Lacasa,et al.  From time series to complex networks: The visibility graph , 2008, Proceedings of the National Academy of Sciences.

[5]  Jing Hu,et al.  Multiscale entropy analysis of biological signals: a fundamental bi-scaling law , 2015, Front. Comput. Neurosci..

[6]  M. Small,et al.  Characterizing pseudoperiodic time series through the complex network approach , 2008 .

[7]  Pouya Manshour,et al.  Complex network approach to fractional time series. , 2015, Chaos.

[8]  José María Amigó,et al.  Detecting Determinism in Time Series with Ordinal Patterns: a Comparative Study , 2010, Int. J. Bifurc. Chaos.

[9]  B. Pompe,et al.  Permutation entropy: a natural complexity measure for time series. , 2002, Physical review letters.

[10]  M Small,et al.  Complex network from pseudoperiodic time series: topology versus dynamics. , 2006, Physical review letters.

[11]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[12]  Jianbo Gao,et al.  Characterizing heart rate variability by scale-dependent Lyapunov exponent. , 2009, Chaos.

[13]  Scott David Greenwald,et al.  The development and analysis of a ventricular fibrillation detector , 1986 .

[14]  Miguel A. F. Sanjuán,et al.  True and false forbidden patterns in deterministic and random dynamics , 2007 .

[15]  Zhongke Gao,et al.  Complex network from time series based on phase space reconstruction. , 2009, Chaos.

[16]  H. B. Mann,et al.  On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other , 1947 .

[17]  W. Hoeffding,et al.  Contributions to Probability and Statistics: Essays in Honor of Harold Hotelling , 1961 .

[18]  Jianbo Gao,et al.  Multiscale Analysis of Heart Rate Variability: A Comparison of Different Complexity Measures , 2010, Annals of Biomedical Engineering.

[19]  R G Mark,et al.  Improved detection and classification of arrhythmias in noise-corrupted electrocardiograms using contextual information , 1990, [1990] Proceedings Computers in Cardiology.

[20]  Michael Small,et al.  Multiscale characterization of recurrence-based phase space networks constructed from time series. , 2012, Chaos.

[21]  W. Kruskal,et al.  Use of Ranks in One-Criterion Variance Analysis , 1952 .

[22]  Yong Tan,et al.  A comparison of two methods for modeling large-scale data from time series as complex networksa) , 2011 .

[23]  Yue Yang,et al.  Complex network-based time series analysis , 2008 .

[24]  L. Zunino,et al.  Discriminating chaotic and stochastic dynamics through the permutation spectrum test. , 2014, Chaos.

[25]  Michael Small,et al.  Time lagged ordinal partition networks for capturing dynamics of continuous dynamical systems. , 2015, Chaos.

[26]  C. Kulp,et al.  Using forbidden ordinal patterns to detect determinism in irregularly sampled time series. , 2016, Chaos.

[27]  Ajit C. Tamhane,et al.  A Comparison of Procedures for Multiple Comparisons of Means with Unequal Variances , 1979 .