Heart Failure Detection Using Immittance Vector Correlation
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Solution Overview
Problem
Current techniques for detecting heart failure in patients with implanted medical devices rely on cardiac pressure estimates or conduction delay estimates derived from immittance signals, which limits the effectiveness of heart failure detection and tracking.
Innovation Solution
An implantable medical device senses physiological signals along multiple vectors, calculates the amount of independent informational content using cross-correlation coefficients, and adjusts therapy or generates warnings based on changes in these coefficients, allowing for the detection and tracking of heart failure without relying on cardiac pressure or conduction delay estimates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cardiac pressure estimates or conduction delay estimates derived from immittance signals are used for heart failure detection, then heart failure can be detected, but the detection effectiveness is limited due to dependence on baseline immittance values
Solution Approach 1:
The patent transforms the detection approach by changing from using absolute immittance values to using correlation coefficients between multiple immittance vectors. This parameter transformation eliminates dependence on baseline immittance values, as correlation coefficients are normalized measures that remain valid regardless of baseline shifts. The system calculates correlation coefficients between pairs of immittance vectors measured across multiple vectors, providing a baseline-independent metric for detecting heart failure.
2Loss of time
If multiple immittance vectors are measured and analyzed, then earlier and more accurate heart failure detection is achieved, but the computational complexity and processing requirements increase
Solution Approach 1:
The patent extracts the essential diagnostic information by calculating correlation coefficients between immittance vectors, which captures the relationships between multiple vectors without requiring complex analysis of each individual vector. This extraction approach reduces the computational burden while preserving the critical information needed for early heart failure detection, as correlation coefficients provide a compact summary of vector relationships.
Solution Approach 2:
The patent moves the analysis from the time domain to the correlation domain, creating a new dimensional representation of the immittance data. By computing correlation coefficients between vectors, the system transforms the problem into a different mathematical space where heart failure detection becomes more sensitive and earlier, while the computational operations remain relatively simple matrix calculations.
Data Source
AI summary
Techniques are provided for detecting heart failure or other medical conditions within a patient using an implantable medical device, such as pacemaker or implantable cardioverter/defibrillator, or external system. In one example, physiological signals, such as immittance-based signals, are sensed within the patient along a plurality of different vectors, and the amount of independent informational content among the physiological signals of the different vectors is determined. Heart failure is then detected by the implantable device based on a significant increase in the amount of independent informational content among the physiological signals. In response, therapy may be controlled, diagnostic information stored, and/or warning signals generated. In other examples, at least some of these functions are performed by an external system.