Refined Multiscale Entropy: Application to 24-h Holter Recordings of Heart Period Variability in Healthy and Aortic Stenosis Subjects

Multiscale entropy (MSE) was proposed to characterize complexity as a function of the time-scale factor tau. Despite its broad use, this technique suffers from two limitations: (1) the artificial MSE reduction due to the coarse graining procedure and (2) the introduction of spurious MSE oscillations due to the suboptimal procedure for the elimination of the fast temporal scales. We propose a refined MSE (RMSE), and we apply it to simulations and to 24-h Holter recordings of heart rate variability (HRV) obtained from healthy and aortic stenosis (AS) groups. The study showed that the refinement relevant to the elimination of the fast temporal scales was more helpful at short scales (spanning the range of short-term HRV oscillations), while that relevant to the procedure of coarse graining was more useful at large scales. In healthy subjects, during daytime, RMSE was smaller at short scales (i.e., tau =1-2) and larger at longer scales (i.e., tau =4-20) than during nighttime. In AS population, RMSE was smaller during daytime both at short and long time scales (i.e., tau = 1 -11) than during nighttime. RMSE was larger in healthy group than in AS population during both daytime (i.e., tau = 2 -9) and nighttime (i.e., tau = 2). RMSE overcomes two limitations of MSE and confirms the complementary information that can be derived by observing complexity as a function of the temporal scale.

[1]  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.

[2]  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.

[3]  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 .

[4]  Ki H. Chon,et al.  Mutual information function assesses autonomic information flow of heart rate dynamics at different time scales , 2005, IEEE Transactions on Biomedical Engineering.

[5]  Roberto Hornero,et al.  Analysis of electroencephalograms in Alzheimer's disease patients with multiscale entropy. , 2006 .

[6]  M. Javorka,et al.  Short-term heart rate complexity is reduced in patients with type 1 diabetes mellitus , 2008, Clinical Neurophysiology.

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

[8]  Pere Caminal,et al.  Multiscale sample entropy in heart rate variability of aortic stenosis patients , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[9]  Niels Wessel,et al.  Intermittently decreased beat-to-beat variability in congestive heart failure. , 2003, Physical review letters.

[10]  T. Brismar,et al.  Comment on "Multiscale entropy analysis of complex physiologic time series". , 2004, Physical review letters.

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

[12]  Derek Abbott,et al.  Scaling Characteristics of Heart Rate Time Series Before the Onset of Ventricular Tachycardia , 2007, Annals of Biomedical Engineering.

[13]  R. Thuraisingham,et al.  On multiscale entropy analysis for physiological data , 2006 .

[14]  Urban Wiklund,et al.  Parasympathetic dysfunction in hypertrophic cardiomyopathy assessed by heart rate variability: comparison between short‐term and 24‐h measurements , 2005, Clinical physiology and functional imaging.

[15]  A. Porta,et al.  Progressive decrease of heart period variability entropy-based complexity during graded head-up tilt. , 2007, Journal of applied physiology.

[16]  A. N. Mamelak,et al.  Long-range temporal correlations in the spontaneous spiking of neurons in the hippocampal-amygdala complex of humans , 2005, Neuroscience.

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

[18]  E Fleck,et al.  Activation of the cardiac renin-angiotensin system and increased myocardial collagen expression in human aortic valve disease. , 2001, Journal of the American College of Cardiology.

[19]  Sergio Cerutti,et al.  Comparison of entropy-based regularity estimators: application to the fetal heart rate signal for the identification of fetal distress , 2006, IEEE Transactions on Biomedical Engineering.

[20]  N. Montano,et al.  Complexity and Nonlinearity in Short-Term Heart Period Variability: Comparison of Methods Based on Local Nonlinear Prediction , 2007, IEEE Transactions on Biomedical Engineering.

[21]  Zhuo Yang,et al.  Long-range correlation of renal sympathetic nerve activity in both conscious and anesthetized rats , 2008, Journal of Neuroscience Methods.

[22]  M Malik,et al.  Is there increased sympathetic activity in patients with hypertrophic cardiomyopathy? , 1995, Journal of the American College of Cardiology.

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

[24]  A. M. Reilly,et al.  Central Mechanisms Underlying Short‐ And Long‐Term Regulation Of The Cardiovascular System , 2002, Clinical and experimental pharmacology & physiology.

[25]  Ewa Orłowska-Baranowska,et al.  How to manage patients with aortic stenosis? , 2007, Cardiology journal.

[26]  Alberto Porta,et al.  Assessment of cardiac autonomic modulation during graded head-up tilt by symbolic analysis of heart rate variability. , 2007, American journal of physiology. Heart and circulatory physiology.

[27]  Cheol-Sung Yoo,et al.  On the physiological validity and the effects of detrending in the multiscale entropy analysis of heart rate variability , 2006 .

[28]  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.