Heart Rate Entropy Measure for CHF Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods lack an effective way to detect imminent congestive heart failure (CHF) using heart rate or pulse rate series, as existing approaches are invasive, computationally intensive, or fail to distinguish normal sinus rhythm from atrial fibrillation in patients with CHF, especially in those with implantable cardioverter-defibrillators where resources are limited.
Innovation Solution
A new measure of heart rate entropy is developed, distinct from sample entropy, which combines multiple data streams using optimized mathematical techniques to analyze RR interval time series, allowing for the early detection of CHF and atrial fibrillation using implanted devices, even with limited computational power.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If invasive monitoring methods or complex computational algorithms are used to detect CHF, then measurement precision is improved, but device complexity and ease of operation deteriorate
Solution Approach 1:
The patent extracts only the essential features needed for CHF detection from complex physiological signals. By focusing on specific characteristics of heart rate variability and rhythm patterns rather than processing entire signal datasets, the system achieves accurate CHF detection with simplified algorithms suitable for implantable devices.
Solution Approach 2:
The patent employs lightweight computational models that can be executed with limited processing power and memory resources. These simplified algorithms consume minimal energy and computational capacity, making them suitable for battery-powered implantable devices with constrained resources while maintaining clinical utility.
2Measurement precision
If comprehensive physiological data analysis is performed, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The system extracts only the most discriminative features from physiological signals for CHF detection. By analyzing specific aspects of heart rate variability and rhythm patterns rather than processing all available physiological data, energy consumption is minimized while maintaining detection accuracy.
Solution Approach 2:
The patent implements partial analysis of physiological data by focusing on key parameters and time windows that are most informative for CHF detection. Rather than continuously processing all physiological signals, the system performs targeted analysis at strategically selected moments, reducing overall energy consumption.
3Measurement precision
If traditional entropy measures like sample entropy are used, then measurement precision is improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent replaces computationally intensive sample entropy calculations with simplified statistical measures that can be computed efficiently on implantable devices. These lighter computational approaches use basic arithmetic operations and require minimal memory, making them suitable for resource-constrained environments while preserving diagnostic accuracy.
Solution Approach 2:
The patent substitutes complex mathematical computations with simpler statistical analyses. By replacing advanced entropy-based methods with more straightforward statistical measures of heart rate variability and rhythm patterns, the system achieves comparable diagnostic performance with significantly reduced computational burden.
Data Source
AI summary
A method for analysis of cardiac rhythm and RR interval time series based on entropy related data and entropy based measures. The information is related to but distinct from entropy, and is derived from histograms of interval match counts in which the y-axis is the frequency of intervals of length in that have the match count given on the x-axis. The phenotype of the histogram informs on the presence of atrial fibrillation or, in the presence of sinus rhythm, the degree of congestive heart failure.