HRVanalysis: A Free Software for Analyzing Cardiac Autonomic Activity

Since the pioneering studies of the 1960s, heart rate variability (HRV) has become an increasingly used non-invasive tool for examining cardiac autonomic functions and dysfunctions in various populations and conditions. Many calculation methods have been developed to address these issues, each with their strengths and weaknesses. Although, its interpretation may remain difficult, this technique provides, from a non-invasive approach, reliable physiological information that was previously inaccessible, in many fields including death and health prediction, training and overtraining, cardiac and respiratory rehabilitation, sleep-disordered breathing, large cohort follow-ups, children's autonomic status, anesthesia, or neurophysiological studies. In this context, we developed HRVanalysis, a software to analyse HRV, used and improved for over 20 years and, thus, designed to meet laboratory requirements. The main strength of HRVanalysis is its wide application scope. In addition to standard analysis over short and long periods of RR intervals, the software allows time-frequency analysis using wavelet transform as well as analysis of autonomic nervous system status on surrounding scored events and on preselected labeled areas. Moreover, the interface is designed for easy study of large cohorts, including batch mode signal processing to avoid running repetitive operations. Results are displayed as figures or saved in TXT files directly employable in statistical softwares. Recordings can arise from RR or EKG files of different types such as cardiofrequencemeters, holters EKG, polygraphs, and data acquisition systems. HRVanalysis can be downloaded freely from the Web page at: https://anslabtools.univ-st-etienne.fr HRVanalysis is meticulously maintained and developed for in-house laboratory use. In this article, after a brief description of the context, we present an overall view of HRV analysis and we describe the methodological approach of the different techniques provided by the software.

[1]  M. Malik,et al.  Sympathovagal balance: a critical appraisal. , 1998, Circulation.

[2]  T. Komatsu,et al.  [Analysis of heart rate variability]. , 2009, Masui. The Japanese journal of anesthesiology.

[3]  Mateus Joffily,et al.  KARDIA: A Matlab software for the analysis of cardiac interbeat intervals , 2010, Comput. Methods Programs Biomed..

[4]  J.L.A. de Carvalho,et al.  Development of a Matlab software for analysis of heart rate variability , 2002, 6th International Conference on Signal Processing, 2002..

[5]  R. Acharya U,et al.  Comprehensive analysis of cardiac health using heart rate signals , 2004, Physiological measurement.

[6]  J. Saul,et al.  Heart rate and muscle sympathetic nerve variability during reflex changes of autonomic activity. , 1990, The American journal of physiology.

[7]  Vincent Pichot,et al.  Dominance in cardiac parasympathetic activity during real recreational SCUBA diving , 2009, European Journal of Applied Physiology.

[8]  I. Birznieks,et al.  Effects of deep and superficial experimentally induced acute pain on muscle sympathetic nerve activity in human subjects , 2009, The Journal of physiology.

[9]  A Malliani,et al.  Instant power spectrum analysis of heart rate variability during orthostatic tilt using a time-/frequency-domain method. , 1997, Circulation.

[10]  A. Camm,et al.  Heart rate variability in relation to prognosis after myocardial infarction: selection of optimal processing techniques. , 1989, European heart journal.

[11]  F. Chouchoul,et al.  Autonomic pain responses during sleep: A study of heart rate variability , 2011 .

[12]  M. Desseilles,et al.  Heart rate variability: a tool to explore the sleeping brain? , 2014, Front. Neurosci..

[13]  Giuseppe Baselli,et al.  Measuring regularity by means of a corrected conditional entropy in sympathetic outflow , 1998, Biological Cybernetics.

[14]  M N Cheung,et al.  Detection of and recovery from errors in cardiac interbeat intervals. , 1981, Psychophysiology.

[15]  Mika P. Tarvainen,et al.  Kubios HRV - Heart rate variability analysis software , 2014, Comput. Methods Programs Biomed..

[16]  V Pichot,et al.  Autonomic pain responses during sleep : a study of heart rate variability , 2010 .

[17]  O Lewis,et al.  Stephen Hales and the measurement of blood pressure. , 1994, Journal of human hypertension.

[18]  Metin Akay,et al.  Wavelet decomposition of cardiovascular signals for baroreceptor function tests in pigs , 2002, IEEE Transactions on Biomedical Engineering.

[19]  M. Malik,et al.  Deceleration capacity of heart rate as a predictor of mortality after myocardial infarction: cohort study , 2006, The Lancet.

[20]  D. Menicucci,et al.  Deriving the respiratory sinus arrhythmia from the heartbeat time series using empirical mode decomposition , 2003, q-bio/0310002.

[21]  J H Felts Stephen Hales and the measurement of blood pressure. , 1977, North Carolina medical journal.

[22]  T Seppänen,et al.  Power-law relationship of heart rate variability as a predictor of mortality in the elderly. , 1998, Circulation.

[23]  E. Hon,et al.  ELECTRONIC EVALUATION OF THE FETAL HEART RATE. VIII. PATTERNS PRECEDING FETAL DEATH, FURTHER OBSERVATIONS. , 1963, American journal of obstetrics and gynecology.

[24]  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 .

[25]  A. Malliani,et al.  Heart rate variability. Standards of measurement, physiological interpretation, and clinical use , 1996 .

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

[27]  P. Stein,et al.  Time domain measurements of heart rate variability. , 1992, Cardiology clinics.

[28]  Hélène Bastuji,et al.  Cardiac Sympathetic Modulation in Response to Apneas/Hypopneas through Heart Rate Variability Analysis , 2014, PloS one.

[29]  Markus Gschwind,et al.  Bradycardia and asystole during generalised interictal EEG discharges. , 2014, Epileptic disorders : international epilepsy journal with videotape.

[30]  T. Seppänen,et al.  Quantitative beat-to-beat analysis of heart rate dynamics during exercise. , 1996, The American journal of physiology.

[31]  Suvi Tiinanen,et al.  Time-frequency representation of cardiovascular signals during handgrip exercise , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[32]  R J Cohen,et al.  Assessment of autonomic regulation in chronic congestive heart failure by heart rate spectral analysis. , 1988, The American journal of cardiology.

[33]  P. Guyenet The sympathetic control of blood pressure , 2006, Nature Reviews Neuroscience.

[34]  J. Legramante,et al.  Revisiting the potential of time-domain indexes in short-term HRV analysis , 2006, Biomedizinische Technik. Biomedical engineering.

[35]  V Pichot,et al.  Wavelet transform to quantify heart rate variability and to assess its instantaneous changes. , 1999, Journal of applied physiology.

[36]  A. Camm,et al.  Heart-rate turbulence after ventricular premature beats as a predictor of mortality after acute myocardial infarction , 1999, The Lancet.

[37]  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.

[38]  Tobias Kaufmann,et al.  ARTiiFACT: a tool for heart rate artifact processing and heart rate variability analysis , 2011, Behavior research methods.

[39]  C Cerutti,et al.  Autonomic nervous system and cardiovascular variability in rats: a spectral analysis approach. , 1991, The American journal of physiology.

[40]  Roberto Sassi,et al.  Assessing nonlinear properties of heart rate variability from short-term recordings: are these measurements reliable? , 2007, Physiological measurement.

[41]  R. Cohen,et al.  Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control. , 1981, Science.

[42]  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.

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

[44]  A Malliani,et al.  Sympathovagal balance: a reappraisal. , 1998, Circulation.

[45]  J. Fleiss,et al.  Power law behavior of RR-interval variability in healthy middle-aged persons, patients with recent acute myocardial infarction, and patients with heart transplants. , 1996, Circulation.

[46]  S. Guzzetti,et al.  Physiological time-series analysis using approximate entropy and sample entropy , 2000 .

[47]  D. Adam,et al.  Assessment of autonomic function in humans by heart rate spectral analysis. , 1985, The American journal of physiology.

[48]  A Voss,et al.  Improved analysis of heart rate variability by methods of nonlinear dynamics. , 1995, Journal of electrocardiology.

[49]  S. Cappelle,et al.  Graded vascular autonomic control versus discontinuous cardiac control during gradual upright tilt. , 2000, Journal of the autonomic nervous system.

[50]  W. Zareba,et al.  Heart rate variability. , 2013, Handbook of clinical neurology.

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

[52]  Leandro Rodríguez Liñares,et al.  An open source tool for heart rate variability spectral analysis , 2011, Comput. Methods Programs Biomed..

[53]  A. Porta,et al.  Nonlinear Indices of Heart Rate Variability in Chronic Heart Failure Patients: Redundancy and Comparative Clinical Value , 2007, Journal of cardiovascular electrophysiology.

[54]  H. Luczak,et al.  An analysis of heart rate variability. , 1973, Ergonomics.

[55]  D L Eckberg,et al.  Mechanisms underlying very-low-frequency RR-interval oscillations in humans. , 1998, Circulation.

[56]  A. Wolf,et al.  Determining Lyapunov exponents from a time series , 1985 .

[57]  G. Billman The LF/HF ratio does not accurately measure cardiac sympatho-vagal balance , 2013, Front. Physio..

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

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

[60]  P. Sleight,et al.  Sympathovagal balance. , 1998, Circulation.

[61]  S. Cerutti,et al.  Complexity analysis of 24 hours heart rate variability time series , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[62]  A. Porta,et al.  Symbolic Dynamics of Heart Rate Variability: A Probe to Investigate Cardiac Autonomic Modulation , 2005, Circulation.

[63]  Frédéric Costes,et al.  Wavelet transform of heart rate variability to assess autonomic nervous system activity does not predict arousal from general anesthesia , 2001, Canadian journal of anaesthesia = Journal canadien d'anesthesie.

[64]  P. Stein,et al.  Origin of Heart Rate Variability and Turbulence: An Appraisal of Autonomic Modulation of Cardiovascular Function , 2011, Front. Physio..

[65]  G. Billman Heart Rate Variability – A Historical Perspective , 2011, Front. Physio..

[66]  A. Porta,et al.  Relationship between spectral components of cardiovascular variabilities and direct measures of muscle sympathetic nerve activity in humans. , 1997, Circulation.

[67]  H V Huikuri,et al.  Prediction of sudden cardiac death by fractal analysis of heart rate variability in elderly subjects. , 2001, Journal of the American College of Cardiology.

[68]  R Maestri,et al.  POLYAN: a computer program for polyparametric analysis of cardio-respiratory variability signals. , 1998, Computer methods and programs in biomedicine.

[69]  Szi-Wen Chen,et al.  A real-time QRS detection method based on moving-averaging incorporating with wavelet denoising , 2006, Comput. Methods Programs Biomed..

[70]  Federico Lombardi,et al.  Heart rate turbulence: standards of measurement, physiological interpretation, and clinical use: International Society for Holter and Noninvasive Electrophysiology Consensus. , 2008, Journal of the American College of Cardiology.

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

[72]  Hon Eh,et al.  ELECTRONIC EVALUATION OF THE FETAL HEART RATE. VIII. PATTERNS PRECEDING FETAL DEATH, FURTHER OBSERVATIONS. , 1963 .