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Multifractal foundations of biomarker discovery for heart disease and stroke
Madhur Mangalam1, Arash Sadri2,3, Junichiro Hayano4
1Division of Biomechanics and Research Development, Department of Biomechanics, and Center for Research in Human Movement Variability, University of Nebraska at Omaha, Omaha, NE, 68182, USA. mmangalam@unomaha.edu.
Insights
This study introduces a new method to create reliable digital biomarkers for diseases like heart disease and stroke by addressing nonergodicity in biological processes. New cascade-dynamical descriptors improve biomarker specificity and generalizability.
Area of Science:
- Biomedical Engineering
- Complex Systems Science
- Digital Health
Background:
- Reliable biomarkers require specificity, generalizability, and reproducibility, which are challenged by nonergodicity in biological processes.
- Current statistical methods assume ergodicity, where measures converge across individuals and time, a condition often violated in biology.
- The lack of reliable biomarkers for leading causes of death like heart disease and stroke highlights the need for novel approaches.
Purpose of the Study:
- To develop a method for deriving generalizable inferences from nonergodic biological phenomena.
- To identify reliable digital biomarkers for heart disease and stroke by addressing ergodicity-breaking dynamics.
- To introduce cascade dynamics as a key factor in ergodicity-breaking and propose methods to overcome it.
Main Methods:
- Proposed capturing cascade dynamics as the origin of ergodicity-breaking in biological processes.
- Assessed hypotheses by challenging the identification of biomarkers for heart disease and stroke.
- Utilized raw R-R interval data and introduced cascade-dynamical descriptors, including the Hurst exponent and multifractal nonlinearity.
Main Results:
- Demonstrated that raw R-R interval data and traditional mean/variance descriptors are nonergodic and non-specific for heart disease and stroke.
- Showed that cascade-dynamical descriptors (Hurst exponent, multifractal nonlinearity) describe heart rate variability more ergodically and are specific.
- Validated the specificity and generalizability of the proposed cascade-dynamical approach for digital health biomarkers.
Conclusions:
- Cascade dynamics are a critical source of ergodicity-breaking in biological processes like heart rate variability.
- The proposed cascade-dynamical descriptors offer a more ergodic and specific approach to digital biomarker discovery.
- This study pioneers the application of ergodicity principles to develop reliable digital biomarkers for health and disease.
Abstract:
Any reliable biomarker has to be specific, generalizable, and reproducible across individuals and contexts. The exact values of such a biomarker must represent similar health states in different individuals and at different times within the same individual to result in the minimum possible false-positive and false-negative rates. The application of standard cut-off points and risk scores across populations hinges upon the assumption of such generalizability. Such generalizability, in turn, hinges upon this condition that the phenomenon investigated by current statistical methods is ergodic, i.e., its statistical measures converge over individuals and time within the finite limit of observations. However, emerging evidence indicates that biological processes abound with nonergodicity, threatening this generalizability. Here, we present a solution for how to make generalizable inferences by deriving ergodic descriptions of nonergodic phenomena. For this aim, we proposed capturing the origin of ergodicity-breaking in many biological processes: cascade dynamics. To assess our hypotheses, we embraced the challenge of identifying reliable biomarkers for heart disease and stroke, which, despite being the leading cause of death worldwide and decades of research, lacks reliable biomarkers and risk stratification tools. We showed that raw R-R interval data and its common descriptors based on mean and variance are nonergodic and non-specific. On the other hand, the cascade-dynamical descriptors, the Hurst exponent encoding linear temporal correlations, and multifractal nonlinearity encoding nonlinear interactions across scales described the nonergodic heart rate variability more ergodically and were specific. This study inaugurates applying the critical concept of ergodicity in discovering and applying digital biomarkers of health and disease.
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