Cardiac Entropy Analysis for Sudden Cardiac Death Prediction
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Solution Overview
Problem
Current methods for analyzing cardiac rhythms, particularly in the frequency domain, fail to account for nonstationarity and irregularities, leading to incomplete understanding of heart rate variability and prediction of health and mortality, as they conceal interactions between mechanisms and fail to quantify self-similar fluctuations effectively.
Innovation Solution
The method involves obtaining ventricular activation (RR) time series from subjects and calculating cardiac entropy using the coefficient of sample entropy (COSEn) to determine health and mortality, allowing for dynamic assessment and creation of treatment plans by monitoring cardiac rhythms and other physiological signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If frequency domain analyses are used to analyze cardiac rhythms, then power spectrum density can be calculated, but nonstationarity and irregularities in the time series are not accounted for, leading to loss of information about self-similar fluctuations and interactions between mechanisms
Solution Approach 1:
The patent transitions from frequency domain parameters (power spectrum density) to time domain parameters (sample entropy, Hurst exponent) to analyze cardiac rhythms. This parameter change enables direct quantification of self-similar fluctuations and nonstationarity without requiring equidistant sampling or interpolation, thereby preserving information about interactions between physiological mechanisms while maintaining measurement precision.
2Measurement precision
If cubic spline interpolation is used to convert interval time series to equidistant samples, then power spectrum density can be estimated, but additional harmonic components are generated in the spectrum
Solution Approach 1:
The patent extracts only the necessary information (sample entropy, Hurst exponent) directly from the irregularly spaced RR interval time series without performing interpolation. This eliminates the harmful artifact of additional harmonic components that are generated by cubic spline interpolation while still obtaining accurate spectral estimates through nonlinear time series analysis methods.
3Ease of operation
If conventional linear methods are used to analyze heart rate variability, then time domain metrics can be established, but irregularities in ventricular activation intervals are not accounted for
Solution Approach 1:
The patent employs dynamic nonlinear analysis methods (sample entropy, Hurst exponent) that can directly handle irregularly spaced time series data without requiring conversion to equidistant samples. This dynamic approach preserves information about irregularities in ventricular activation intervals while maintaining ease of operation through standardized computational procedures.
Data Source
AI summary
A method of determining health and mortality includes obtaining a ventricular activation (RR) time series from a subject for multiple temporal intervals. The method also includes calculating a cardiac entropy in the RR time series over the temporal intervals using coefficient of sample entropy (COSEn). Additionally, the method includes comparing the cardiac entropy between the intervals to determine health and mortality. The absolute and relative changes in entropy over a patient's follow up period provide dynamic information regarding health and mortality risk. The determination of health and mortality can then be used to create a treatment plan for the subject.