Early detection of arrhythmia using chaos theory

Principal Investigator: Farhad R. Nezami

Authors: Mohammadmostafa Asheghan, Farhad R. Nezami
Lay Abstract

Chaos is a special behavior appearing in some nonlinear dynamic systems, including biological organisms. The states of a chaotic system have a random-like behavior, yet the system is deemed predictable. Some certain changes in parameters of an ordinary periodic system which shows repeated, orderly behavior can result in chaotic dynamics. What happens in this transition is increasing the number of peak values which is theoretically called bifurcation. The main idea in this research is using this mathematical fact to better understand arrhythmia. Arrhythmia is any abnormality in electrical activity of the heart, including irregular heartbeat, that is shown in this work to be chaotic, non-random, and thus predictable. On the other hand, a healthy heart demonstrates a clear cyclic behavior and is categorized as an ordinary periodic system with well-ordered behavior visualized by electrocardiograms. Putting these together, the bifurcation coincides the stage in which a healthy heart may move towards being arrhythmic, while it still apparently shows a healthy behavior. This can potentially be used for early detection of arrhythmia. This novel approach is excitingly based on a rigid mathematical backbone which is experimentally validated, and could provide a deeper insight about arrhythmia and predict it in clinical practice.

Scientific Abstract

Heart arrhythmia is a malfunction in the electrical impulses that coordinate heartbeats resulting in irregular, too fast or too slow heartbeats affecting more than 2% of adult population. Arrhythmias can have medical, physical, emotional, genetic or even unknown causes. In addition to a number of experimental and clinical studies, there has been a load of efforts and interests in the theoretical simulations of this electrophysiological phenomena based on mathematical formulas aiming to understand it and enhance therapy. Herein, we developed a theoretical platform to mathematically show that the behavior of an arrhythmic cardiac signal is not random. We first extracted processed data from the raw arrhythmic heartbeat signal. An inhouse platform to detect chaos was developed and then applied to the data to demonstrate chaotic nature of the signal. Having the chaotic dynamics of the signal confirmed, we then applied all known properties of chaotic systems to this signal particularly focusing on bifurcation, that is the transient behavior when an ordinary system starts getting chaotic. We posit that while the ordinary heart is a neat periodic system opposing a chaotic arrhythmia, the detection of bifurcation in an apparently healthy heartbeat signal could detect arrythmia at early stages.

Clinical Implications
This research explores the nature of arrhythmia and, in particular, suggests an algorithm for its early detection based solely on the cardiac signals. Future extension of this work envisions leveraging the use of wearable devices for early arrythmia prediction.

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