Multiscale Entropy Analysis: Application to Cardio-Respiratory Coupling

It is known that in pathological conditions, physiological systems develop changes in the multiscale properties of physiological signals. However, in real life, little is known about how changes in the function of one of the two coupled physiological systems induce changes in function of the other one, especially on their multiscale behavior. Hence, in this work we aimed to examine the complexity of cardio-respiratory coupled systems control using multiscale entropy (MSE) analysis of cardiac intervals MSE (RR), respiratory time series MSE (Resp), and synchrony of these rhythms by cross multiscale entropy (CMSE) analysis, in the heart failure (HF) patients and healthy subjects. We analyzed 20 min of synchronously recorded RR intervals and respiratory signal during relaxation in the supine position in 42 heart failure patients and 14 control healthy subjects. Heart failure group was divided into three subgroups, according to the RR interval time series characteristics (atrial fibrillation (HFAF), sinus rhythm (HFSin), and sinus rhythm with ventricular extrasystoles (HFVES)). Compared with healthy control subjects, alterations in respiratory signal properties were observed in patients from the HFSin and HFVES groups. Further, mean MSE curves of RR intervals and respiratory signal were not statistically different only in the HFSin group (p = 0.43). The level of synchrony between these time series was significantly higher in HFSin and HFVES patients than in control subjects and HFAF patients (p < 0.01). In conclusion, depending on the specific pathologies, primary alterations in the regularity of cardiac rhythm resulted in changes in the regularity of the respiratory rhythm, as well as in the level of their asynchrony.

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

[2]  Zhenhu Liang,et al.  Multiscale permutation entropy analysis of EEG recordings during sevoflurane anesthesia , 2010, Journal of neural engineering.

[3]  Thomas Penzel,et al.  Phase transitions in physiologic coupling , 2012, Proceedings of the National Academy of Sciences.

[4]  M. Rosenblum,et al.  Identification of coupling direction: application to cardiorespiratory interaction. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[5]  Amir Bashan,et al.  Network physiology reveals relations between network topology and physiological function , 2012, Nature Communications.

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

[7]  Plamen Ch. Ivanov,et al.  Three independent forms of cardio-respiratory coupling: transitions across sleep stages , 2014, Computing in Cardiology 2014.

[8]  Ku Ruhana Ku-Mahamud,et al.  Rule pruning techniques in the ant-miner classification algorithm and its variants: A review , 2018, 2018 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE).

[9]  Ronny P Bartsch,et al.  Quantifying cardio-respiratory phase synchronization—a comparison of five methods using ECGs of post-infarction patients , 2017, Physiological measurement.

[10]  D. Abásolo,et al.  Nonlinear measure of synchrony between blood oxygen saturation and heart rate from nocturnal pulse oximetry in obstructive sleep apnoea syndrome , 2009, Physiological measurement.

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

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

[13]  Kendall F Morris,et al.  Cardiorespiratory coupling: common rhythms in cardiac, sympathetic, and respiratory activities. , 2014, Progress in brain research.

[14]  Mirjana M. Platiša,et al.  Differentiation of Heart Failure Patients by the Ratio of the Scaling Exponents of Cardiac Interbeat Intervals , 2019, Front. Physiol..

[15]  Steven M. Pincus.  Approximate entropy as a measure of irregularity for psychiatric serial metrics. , 2006, Bipolar disorders.

[16]  Alberto Porta,et al.  Multiscale entropy analysis of heart rate variability in heart failure, hypertensive, and sinoaortic-denervated rats: classical and refined approaches. , 2016, American journal of physiology. Regulatory, integrative and comparative physiology.

[17]  J. Kurths,et al.  Heartbeat synchronized with ventilation , 1998, Nature.

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

[19]  C. Mady,et al.  Breathing disorders in congestive heart failure: gender, etiology and mortality. , 2008, Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologicas.

[20]  Pere Caminal,et al.  Refined Multiscale Entropy: Application to 24-h Holter Recordings of Heart Period Variability in Healthy and Aortic Stenosis Subjects , 2009, IEEE Transactions on Biomedical Engineering.

[21]  Mirjana M. Platiša,et al.  Reflection of heart rate regulation on linear and nonlinear heart rate variability measures , 2006, Physiological measurement.

[22]  Daniele Marinazzo,et al.  Multiscale analysis of short term heart beat interval, arterial blood pressure, and instantaneous lung volume time series , 2007, Artif. Intell. Medicine.

[23]  G. Schmidt,et al.  Heart rate turbulence. , 2003, Journal of electrocardiology.

[24]  Uncoupling of cardiac and respiratory rhythm in atrial fibrillation , 2016, Biomedizinische Technik. Biomedical engineering.

[25]  Aneta Stefanovska,et al.  Alterations in the coupling functions between cortical and cardio-respiratory oscillations due to anaesthesia with propofol and sevoflurane , 2016, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[26]  Mirjana M. Platiša,et al.  Bidirectional Cardio-Respiratory Interactions in Heart Failure , 2018, Front. Physiol..

[27]  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.

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

[29]  I. Homma,et al.  Breathing rhythms and emotions , 2008, Experimental physiology.

[30]  Mirjana M. Platiša,et al.  Nonlinear properties of cardiac rhythm and respiratory signal under paced breathing in young and middle-aged healthy subjects. , 2014, Medical engineering & physics.

[31]  Mirjana M. Platiša,et al.  Dependence of heart rate variability on heart period in disease and aging , 2006, Physiological measurement.

[32]  P. Rizzon,et al.  Respiratory systolic pressure variability during atrial fibrillation and sinus rhythm. , 1999, Hypertension.

[33]  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.