Modified Distribution Entropy as a Complexity Measure of Heart Rate Variability (HRV) Signal

The complexity of a heart rate variability (HRV) signal is considered an important nonlinear feature to detect cardiac abnormalities. This work aims at explaining the physiological meaning of a recently developed complexity measurement method, namely, distribution entropy (DistEn), in the context of HRV signal analysis. We thereby propose modified distribution entropy (mDistEn) to remove the physiological discrepancy involved in the computation of DistEn. The proposed method generates a distance matrix that is devoid of over-exerted multi-lag signal changes. Restricted element selection in the distance matrix makes “mDistEn” a computationally inexpensive and physiologically more relevant complexity measure in comparison to DistEn.

[1]  Marimuthu Palaniswami,et al.  Effect of data length and bin numbers on distribution entropy (DistEn) measurement in analyzing healthy aging , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[2]  J. Andrew Taylor,et al.  The physiological basis and measurement of heart rate variability in humans , 2016, Journal of Physiological Anthropology.

[3]  Claudia Lerma,et al.  Poincaré plot indexes of heart rate variability capture dynamic adaptations after haemodialysis in chronic renal failure patients. , 2003, Clinical physiology and functional imaging.

[4]  Jeffrey M. Hausdorff,et al.  Multiscale entropy analysis of human gait dynamics. , 2003, Physica A.

[5]  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.

[6]  M Tulppo,et al.  Abnormalities in beat to beat complexity of heart rate dynamics in patients with a previous myocardial infarction. , 1996, Journal of the American College of Cardiology.

[7]  Qichun Zhang,et al.  Entropy-based Iterative Learning Estimation for Stochastic Non-linear Systems and Its Application to Neural Membrane Potential Interaction , 2019, 2019 1st International Conference on Industrial Artificial Intelligence (IAI).

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

[9]  P. Castiglioni,et al.  How the threshold “r” influences approximate entropy analysis of heart-rate variability , 2008, 2008 Computers in Cardiology.

[10]  D. T. Kaplan,et al.  Aging and the complexity of cardiovascular dynamics. , 1991, Biophysical journal.

[11]  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.

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

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

[14]  Marimuthu Palaniswami,et al.  Risk stratification of cardiac autonomic neuropathy based on multi-lag Tone–Entropy , 2013, Medical & Biological Engineering & Computing.

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

[16]  Marimuthu Palaniswami,et al.  Distribution Entropy (DistEn): A complexity measure to detect arrhythmia from short length RR interval time series , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[17]  Ong Wai Sing,et al.  Heart rate analysis in normal subjects of various age groups , 2004, Biomedical engineering online.

[18]  Andreas Holzinger,et al.  Selection of entropy-measure parameters for knowledge discovery in heart rate variability data , 2014, BMC Bioinformatics.

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

[20]  E Kristal-Boneh,et al.  Heart rate variability in health and disease. , 1995, Scandinavian journal of work, environment & health.

[21]  Marimuthu Palaniswami,et al.  Stability, Consistency and Performance of Distribution Entropy in Analysing Short Length Heart Rate Variability (HRV) Signal , 2017, Front. Physiol..

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

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

[24]  H. Huikuri,et al.  Altered complexity and correlation properties of R-R interval dynamics before the spontaneous onset of paroxysmal atrial fibrillation. , 1999, Circulation.

[25]  Yudong Zhang,et al.  Entropy Analysis of Short-Term Heartbeat Interval Time Series during Regular Walking , 2017, Entropy.

[26]  Bruce J. West,et al.  Applications of Nonlinear Dynamics to Clinical Cardiology a , 1987, Annals of the New York Academy of Sciences.

[27]  H. Huikuri,et al.  Fractal and Complexity Measures of Heart Rate Variability , 2005, Clinical and experimental hypertension.

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

[29]  Zhengtao Ding,et al.  RBFNN-Based Minimum Entropy Filtering for a Class of Stochastic Nonlinear Systems , 2020, IEEE Transactions on Automatic Control.

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

[31]  Dingchang Zheng,et al.  Assessing the complexity of short-term heartbeat interval series by distribution entropy , 2014, Medical & Biological Engineering & Computing.

[32]  Hong-Bo Xie,et al.  Measuring time series regularity using nonlinear similarity-based sample entropy , 2008 .