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Fragmentation Of The Heartbeat: Probing The Breakdown Of Biologic Time With Aging And Disease | Science Trends

Fragmentation Of The Heartbeat: Probing The Breakdown Of Biologic Time With Aging And Disease

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The trace of the human heartbeat, a dynamical signature of life itself, is regulated by an exquisitely complex “clock” called the sino-atrial (SA) node. The cells comprising this cardiac timekeeper, like those in all biologic pacemakers, are nonlinear oscillators. It is widely assumed that the SA node beats with metronomic regularity.

However, even under resting conditions and during sleep, the time period between beats shows subtle, non-random fluctuations. These correlation properties give the healthy heartbeat a kind of swaying or “fluent” appearance in time series graphs.

While the variations in heart rate have been of interest for decades or longer, this signal continues to reveal surprising properties that may provide useful information about health status, as well as about how the body’s oscillators break down with senescence and pathology.

From basic and translational perspectives, tracking the behavior of the heart’s intrinsic pacemaker is uniquely valuable. Heartbeat recordings are among the most accessible biologic signals, requiring only continuous recordings of the electrocardiogram (ECG). Furthermore, the variations in heartbeat dynamics under healthy conditions are self-regulated by an orchestral ensemble, including neurohormonal, respiratory, and cardiac components. Thus, the heartbeat cadence may provide a portal through which to unlock key aspects of our systems biology in physiologic and pathologic settings.

Our recent work on heartbeat dynamics has uncovered an intriguing class of abnormalities, grouped under the rubric of heart rate fragmentation, which appears to provide biomarkers of electrophysiologic instability. The basic mechanisms of heart rate fragmentation remain to be delineated but likely relate to electrophysiologic, inflammatory and mechanical perturbations, singly or in combination.


Of note, clinicians are very unlikely to diagnose fragmentation patterns from clinical ECG displays, which are only 10-sec in duration, or even from visual inspection of longer cardiac monitors recordings obtained in critical care units. But the jagged profiles of fragmentation, reflecting abrupt changes in heart rate acceleration not attributable physiologic control mechanisms, are clearly evident in heart rate time series.

Follow-up studies are now aimed at validating these findings and seeing if proposed new measures of heart rate fragmentation can be used to enhance the prediction of adverse outcomes due to cardiovascular and other systemic diseases, as well as to assess the aging process.

These two studies, Heart Rate Fragmentation: A New Approach to the Analysis of Cardiac Interbeat Interval Dynamics and Heart Rate Fragmentation: A Symbolic Dynamical Approach were recently published in the journal Frontiers in Physiology.


About The Author

Presently, I am an Assistant Professor of Medicine at Harvard Medical School, Division of Interdisciplinary Medicine and Biotechnology, Beth Israel Deaconess Medical Center. I am also Co-Director of the Margret and H. A. Rey Institute for Nonlinear Dynamics in Medicine.

I concentrated in Physics at the University of Lisbon. In 2000, while a PhD student there, I was awarded a Fulbright grant and enrolled in the Graduate School of Arts and Sciences, Harvard University, as a special student. I completed my PhD dissertation under the supervision of Professor Eduardo Ducla Soares from the University of Lisbon, in conjunction with Dr. C.-K. Peng and Dr. Ary L. Goldberger from Harvard Medical School. My general area of research is in complex systems (nonlinear dynamics and statistical physics).

My specific focus is in biomedical applications, including improving diagnostics and monitoring of instabilities in physiologic control. The work includes the study of life-threatening cardiac diseases and also neurological pathologies related to gait disorders and seizures. This work is at the interdisciplinary crossroads of contemporary physics, bioengineering, physiology, biology and clinical medicine.

A hallmark of such complex systems is their multiscale organization, both temporally and spatially. However, the ability to model, monitor and control complex dynamics is still in its infancy and poses some of the most daunting challenges in contemporary science. My work is directed at developing (i) methods to quantify multiscale properties of complex signals, (ii) models of physiologic control that account for these properties under healthy conditions and their changes with pathology and aging, and (iii) novel indexes for risk stratification and monitoring of pharmacologic and non-pharmacologic interventions.

Over the past few years, my collaborators and I have developed quantitative algorithms to probe some of the generic features of complex systems and applied these tools to the understanding of the underlying system dynamics. We have introduced multiscale entropy and time irreversibility analysis techniques and applied them to the study of the cardiac dynamics of healthy subjects and patients with different type of pathologies. The former technique quantifies the information content of a signal across multiple time scales and the latter quantifies the degree of temporal irreversibility over multiple time scales. Time irreversibility is a fundamental property of open dissipative systems that operate far from equilibrium.

Ary Louis Goldberger is a physician-educator, whose collaborative research work is at the interface of biomedicine and complexity science (nonlinear dynamics). He holds a BA from Harvard College and an MD from Yale Medical School. He did his clinical training in internal medicine and cardiovascular disease at Yale–New Haven Hospital and at the University of California, San Diego, respectively. He currently serves as Professor of Medicine at Harvard Medical School and was one of the Core Founding Faculty (2010-2015) of the Wyss Institute for Biologically Inspired Engineering at Harvard University.

Goldberger is founding and current Principal Investigator (with R. G. Mark at MIT) of the NIH-sponsored Research Resource for Complex Physiologic Signals. The “Big Data” PhysioNet Resource is the first and remains the largest repository of free, open-access databases and open-source computational tools devoted to complex signals informatics. In addition, he is founding and current Director of the Margret and H.A. Rey Institute for Nonlinear Dynamics in Physiology and Medicine at Beth Israel Deaconess Medical Center (BIDMC) in Boston.