Thoracic Impedance Vector Clustering for Cardiac Decompensation Detection
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Solution Overview
Problem
Current cardiac decompensation detection methods face challenges in providing accurate and sensitive advance notification of imminent cardiac decompensation, particularly due to the confounding effects of global fluid level changes on impedance data, and require dedicated sensors for posture detection.
Innovation Solution
The system obtains thoracic impedance data from multiple vectors over time windows, forming data clusters to minimize fluid level effects and provide posture status without needing additional sensors, by comparing impedance vectors during postural changes and using these comparisons to identify trends indicative of cardiac decompensation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If thoracic impedance data is used to detect cardiac decompensation, then detection capability is provided, but measurement precision deteriorates due to confounding effects of global fluid level changes
Solution Approach 1:
The patent segments the thoracic impedance measurement into multiple independent impedance vectors (e.g., first impedance vector and second impedance vector with different electrode configurations). By analyzing each vector separately and comparing their individual changes rather than relying on a single global impedance measurement, the system can distinguish between fluid level changes affecting all vectors equally versus local pathology affecting specific vectors, thereby improving measurement precision while maintaining detection capability.
2Measurement precision
If multiple impedance vectors are compared to minimize fluid level effects, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent makes the existing electrode system multi-functional by using the same electrodes that deliver pacing or defibrillation therapy to also serve as sensing elements for measuring multiple impedance vectors. This allows the device to perform posture detection and cardiac decompensation monitoring without adding dedicated sensors, thereby improving measurement precision while avoiding the complexity increase that would result from adding separate sensing hardware.
3Measurement precision
If dedicated posture sensors are added to improve posture detection, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent enables the impedance measurement system to self-determine posture information by analyzing the relative changes in multiple impedance vectors. The system uses the body's own electrical properties and the known geometry of electrode placements to infer posture without requiring external or dedicated posture sensors. This allows posture detection functionality to be achieved through the existing therapy delivery electrodes, improving measurement precision while avoiding additional device complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the specificity of cardiac decompensation prediction and provides a more sensitive advance notification of cardiac decompensation risk, while eliminating the need for dedicated posture sensors, thereby improving the accuracy and efficiency of cardiac rhythm management devices.
Implementation Method 1
obtaining a function of first thoracic impedance data and second thoracic impedance data... obtaining third thoracic impedance data using at least two thoracic impedance vectors... comparing at least two impedance vectors
Data Source
AI summary
Physiological data, such as thoracic impedance data, can be obtained over a first time window to establish a baseline, or can be used to form one or more data clusters. Additional physiological data, such as thoracic impedance test data acquired over a later time window, can be obtained and compared to the baseline or data clusters to determine an indication of worsening heart failure. In an example, a quantitative attribute of one or more data clusters can be monitored and used to provide an indication of worsening heart failure. A posture discrimination metric can be obtained, such as using the physiological data obtained over the first time window. The additional physiological data, such as can be obtained over a second time window, can be compared to the posture discrimination metric to provide a patient posture status.