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Complex patterns of abnormal heartbeats
Verena Schulte-Frohlinde1, Yosef Ashkenazy, Ary L Goldberger
1Center for Polymer Studies, Department of Physics, Boston University, Boston, Massachusetts 02215, USA. frohlind@argento.bu.edu
Insights
Frequent abnormal heartbeats increase sudden cardiac death risk. This study introduces "heartprints," a visual method to analyze heart rhythm patterns and understand the mechanisms behind cardiac arrhythmias.
Area of Science:
- Cardiology
- Computational Biology
- Biophysics
Background:
- Frequent abnormal heartbeats (cardiac arrhythmias) are linked to increased sudden cardiac death risk.
- The underlying electrophysiologic mechanisms of many cardiac arrhythmias remain poorly understood.
- Current methods for analyzing heart rhythm patterns have limitations in revealing complex dynamics.
Purpose of the Study:
- To develop a visual and qualitative method for displaying statistical properties of abnormal heartbeats.
- To introduce dynamical "heartprints" for pattern recognition in long clinical cardiac records.
- To assess the potential of these heartprints in elucidating the mechanisms of cardiac arrhythmias.
Main Methods:
- Development of a visual and qualitative method to display statistical properties of abnormal heartbeats.
- Introduction of dynamical "heartprints" to analyze patterns in approximately 10^5 heartbeats from clinical records.
- Comparison of clinical data patterns with simulations from three models: random, fixed-interval, and independent oscillator generation of abnormal heartbeats.
Main Results:
- Dynamical "heartprints" reveal characteristic patterns in clinical cardiac records.
- The study tested the ability of three distinct models to reproduce the statistical features observed in clinical heartprints.
- Limitations of current models in comprehensively simulating clinical cardiac arrhythmia patterns were identified.
Conclusions:
- The developed "heartprint" method offers a novel approach to analyze cardiac arrhythmia dynamics.
- This method can be used to test and refine mathematical models of arrhythmogenesis.
- The findings contribute to a better understanding of the underlying electrophysiologic mechanisms driving cardiac arrhythmias.
Abstract:
Individuals having frequent abnormal heartbeats interspersed with normal heartbeats may be at an increased risk of sudden cardiac death. However, mechanistic understanding of such cardiac arrhythmias is limited. We present a visual and qualitative method to display statistical properties of abnormal heartbeats. We introduce dynamical "heartprints" which reveal characteristic patterns in long clinical records encompassing approximately 10(5) heartbeats and may provide information about underlying mechanisms. We test if these dynamics can be reproduced by model simulations in which abnormal heartbeats are generated (i) randomly, (ii) at a fixed time interval following a preceding normal heartbeat, or (iii) by an independent oscillator that may or may not interact with the normal heartbeat. We compare the results of these three models and test their limitations to comprehensively simulate the statistical features of selected clinical records. This work introduces methods that can be used to test mathematical models of arrhythmogenesis and to develop a new understanding of underlying electrophysiologic mechanisms of cardiac arrhythmia.