Applications of complexity analysis in clinical heart failure

Heart failure is known to influence heart rhythm in patients. Complexity analysis techniques, including techniques associated with entropy, have great potential for providing a better understanding of cardiac rhythms, and for helping research in this area. We review the analysis principles of conventional time-domain analysis, frequency-domain analysis and of newer complexity analysis. We then illustrate the techniques using real clinical data, allowing a comparison of the techniques, and also of the differences between normal heart rate variability and that associated with heart failure.

[1]  Li Zhang,et al.  Measuring synchronization in coupled simulation and coupled cardiovascular time series: A comparison of different cross entropy measures , 2015, Biomed. Signal Process. Control..

[2]  Dingchang Zheng,et al.  Analysis of heart rate variability using fuzzy measure entropy , 2013, Comput. Biol. Medicine.

[3]  Sergio Cerutti,et al.  Entropy, entropy rate, and pattern classification as tools to typify complexity in short heart period variability series , 2001, IEEE Transactions on Biomedical Engineering.

[4]  S M Pincus,et al.  Approximate entropy as a measure of system complexity. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[5]  A L Goldberger,et al.  Physiological time-series analysis: what does regularity quantify? , 1994, The American journal of physiology.

[6]  Roberto Maestri,et al.  Clinical impact of evaluation of cardiovascular control by novel methods of heart rate dynamics , 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[7]  A. Malliani,et al.  Cardiovascular Neural Regulation Explored in the Frequency Domain , 1991, Circulation.

[8]  P. Binkley,et al.  Parasympathetic withdrawal is an integral component of autonomic imbalance in congestive heart failure: demonstration in human subjects and verification in a paced canine model of ventricular failure. , 1991, Journal of the American College of Cardiology.

[9]  Madalena Costa,et al.  Multiscale entropy analysis of complex physiologic time series. , 2002, Physical review letters.

[10]  C. Peng,et al.  Fractal analysis of heart rate dynamics as a predictor of mortality in patients with depressed left ventricular function after acute myocardial infarction. TRACE Investigators. TRAndolapril Cardiac Evaluation. , 1999, The American journal of cardiology.

[11]  A Garfinkel,et al.  Chaos and chaos control in biology. , 1994, The Journal of clinical investigation.

[12]  A M Tonkin,et al.  Application of the Poincaré plot to heart rate variability: a new measure of functional status in heart failure. , 1995, Australian and New Zealand journal of medicine.

[13]  A. Porta,et al.  Linear and non-linear 24 h heart rate variability in chronic heart failure , 2000, Autonomic Neuroscience.

[14]  Hai Liu,et al.  A novel encoding Lempel-Ziv complexity algorithm for quantifying the irregularity of physiological time series , 2016, Comput. Methods Programs Biomed..

[15]  Shoushui Wei,et al.  Determination of Sample Entropy and Fuzzy Measure Entropy Parameters for Distinguishing Congestive Heart Failure from Normal Sinus Rhythm Subjects , 2015, Entropy.

[16]  Nathaniel H. Hunt,et al.  The Appropriate Use of Approximate Entropy and Sample Entropy with Short Data Sets , 2012, Annals of Biomedical Engineering.

[17]  D. J. Veldhuisen,et al.  Prognostic value of heart rate variability and ventricular arrhythmias during 13-year follow-up in patients with mild to moderate heart failure , 2009, Clinical research in cardiology : official journal of the German Cardiac Society.

[18]  H. Stanley,et al.  Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. , 1995, Chaos.

[19]  Heikki V Huikuri,et al.  Clinical applicability of heart rate variability analysis by methods based on nonlinear dynamics. , 2002, Cardiac electrophysiology review.

[20]  Changchun Liu,et al.  Automatic detection of atrial fibrillation using R-R interval signal , 2011, 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI).

[21]  G N Arbolishvili,et al.  [Heart rate variability in chronic heart failure and its role in prognosis of the disease.]. , 2006, Kardiologiia.

[22]  M. Masè,et al.  An integrated approach based on uniform quantization for the evaluation of complexity of short-term heart period variability: Application to 24 h Holter recordings in healthy and heart failure humans. , 2007, Chaos.

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

[24]  Danilo P Mandic,et al.  Multivariate multiscale entropy: a tool for complexity analysis of multichannel data. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[25]  Pere Caminal,et al.  Methods derived from nonlinear dynamics for analysing heart rate variability , 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[26]  Weiting Chen,et al.  Measuring complexity using FuzzyEn, ApEn, and SampEn. , 2009, Medical engineering & physics.

[27]  Roberto Hornero,et al.  Analysis of EEG background activity in Alzheimer's disease patients with Lempel-Ziv complexity and central tendency measure. , 2006, Medical engineering & physics.

[28]  H. Huikuri,et al.  Fractal analysis and time- and frequency-domain measures of heart rate variability as predictors of mortality in patients with heart failure. , 2001, The American journal of cardiology.

[29]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.

[30]  L. Amaral,et al.  Multifractality in human heartbeat dynamics , 1998, Nature.

[31]  H. Azami,et al.  Refined composite multivariate generalized multiscale fuzzy entropy: A tool for complexity analysis of multichannel signals , 2017 .

[32]  A. Kadish,et al.  Dissociation of heart rate variability from parasympathetic tone. , 1994, The American journal of physiology.

[33]  T. Musha,et al.  1/f Fluctuation of Heartbeat Period , 1982, IEEE Transactions on Biomedical Engineering.

[34]  J. Cohn,et al.  Prognosis in congestive heart failure. , 1994, Journal of cardiac failure.

[35]  Jagmeet P. Singh,et al.  Reduced heart rate variability and new-onset hypertension: insights into pathogenesis of hypertension: the Framingham Heart Study. , 1998, Hypertension.

[36]  Roberto Hornero,et al.  Interpretation of the Lempel-Ziv Complexity Measure in the Context of Biomedical Signal Analysis , 2006, IEEE Transactions on Biomedical Engineering.

[37]  J. Kurths,et al.  The application of methods of non-linear dynamics for the improved and predictive recognition of patients threatened by sudden cardiac death. , 1996, Cardiovascular research.

[38]  Chengyu Liu,et al.  Comparison of different threshold values r for approximate entropy: application to investigate the heart rate variability between heart failure and healthy control groups , 2011, Physiological measurement.

[39]  J. Hadamard,et al.  Les surfaces a courbures opposees et leurs lignes geodesique , 1898 .

[40]  R. Prescott,et al.  Prospective study of heart rate variability and mortality in chronic heart failure: results of the United Kingdom heart failure evaluation and assessment of risk trial (UK-heart). , 1998, Circulation.

[41]  Volkan Tuzcu,et al.  Decrease in the heart rate complexity prior to the onset of atrial fibrillation. , 2006, Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology.

[42]  Madalena Costa,et al.  Multiscale entropy analysis of biological signals. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

[44]  Raimon Jané,et al.  Index for estimation of muscle force from mechanomyography based on the Lempel-Ziv algorithm. , 2013, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[45]  U. Rajendra Acharya,et al.  Heart rate variability: a review , 2006, Medical and Biological Engineering and Computing.

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

[47]  Marimuthu Palaniswami,et al.  Poincaré plot interpretation using a physiological model of HRV based on a network of oscillators. , 2002, American journal of physiology. Heart and circulatory physiology.

[48]  Pekka Uotila,et al.  Relation of heart rate dynamics to the occurrence of myocardial ischemia after coronary artery bypass grafting. , 2002, The American journal of cardiology.

[49]  G. Breithardt,et al.  Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. , 1996 .

[50]  W. Stevenson,et al.  Complex heart rate variability and serum norepinephrine levels in patients with advanced heart failure. , 1994, Journal of the American College of Cardiology.

[51]  D. Levy,et al.  Predicting survival in heart failure case and control subjects by use of fully automated methods for deriving nonlinear and conventional indices of heart rate dynamics. , 1997, Circulation.

[52]  Chengyu Liu,et al.  Evaluation method for heart failure using RR sequence normalized histogram , 2011, 2011 Computing in Cardiology.

[53]  Chengyu Liu,et al.  Using Fuzzy Measure Entropy to improve the stability of traditional entropy measures , 2011, 2011 Computing in Cardiology.

[54]  Shingo Kawasaki,et al.  Very low frequency power of heart rate variability is a powerful predictor of clinical prognosis in patients with congestive heart failure. , 2004, Circulation journal : official journal of the Japanese Circulation Society.

[55]  Danilo P. Mandic,et al.  Multivariate Multiscale Entropy Analysis , 2012, IEEE Signal Processing Letters.

[56]  A L Goldberger,et al.  Fractal correlation properties of R-R interval dynamics and mortality in patients with depressed left ventricular function after an acute myocardial infarction. , 2000, Circulation.

[57]  M. P. Griffin,et al.  Sample entropy analysis of neonatal heart rate variability. , 2002, American journal of physiology. Regulatory, integrative and comparative physiology.

[58]  R. Maestri,et al.  Short-Term Heart Rate Variability Strongly Predicts Sudden Cardiac Death in Chronic Heart Failure Patients , 2003, Circulation.

[59]  Chi-Sang Poon,et al.  Decrease of cardiac chaos in congestive heart failure , 1997, Nature.

[60]  F. Lombardi,et al.  Chaos theory, heart rate variability, and arrhythmic mortality. , 2000, Circulation.

[61]  A. Tonkin,et al.  Poincaré plot of heart rate variability allows quantitative display of parasympathetic nervous activity in humans. , 1996, Clinical science.