Endocardial Acceleration Signal Analysis for Heart Failure Monitoring
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Solution Overview
Problem
Current medical devices fail to effectively assess the impact of central sleep apnea on heart failure patients, particularly in identifying non-responders to Cardiac Resynchronization Therapy (CRT) and detecting early signs of cardiac decompensation, leading to ineffective or harmful treatments and potential hospitalizations.
Innovation Solution
A device that uses endocardial acceleration (EA) signal analysis to calculate a variability index, which compares the standard deviation of EA parameter values during sleep to a reference value, triggering alerts when a predetermined threshold is exceeded, allowing for the rapid discrimination of non-responder patients and reassessment of therapy to prevent deterioration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current medical devices use traditional monitoring methods, then device complexity is reduced, but measurement precision and reliability of heart failure diagnosis are insufficient
Solution Approach 1:
The patent introduces an intermediary processing layer that analyzes the relationship between EA signal variations and sleep apnea indicators. This intermediary analysis layer enables precise diagnosis by connecting cardiac signals with respiratory patterns, thereby improving measurement precision without requiring direct complex multi-sensor integration.
Solution Approach 2:
The patent replaces traditional mechanical/physical monitoring approaches with signal processing and algorithmic analysis of the EA signal. By substituting direct physical measurement with computational analysis of signal patterns, the system achieves high diagnostic precision while maintaining relatively simple device architecture.
2Reliability
If long-term monitoring of clinical status is implemented, then reliability of heart failure assessment is improved, but loss of time and energy increase
Solution Approach 1:
The patent performs preliminary analysis of EA signal variations during sleep periods to predict clinical status changes before they manifest as overt heart failure decompensation. By detecting early subtle changes in the EA signal pattern, the system enables proactive intervention without requiring continuous long-term monitoring, thus reducing time loss while maintaining high reliability.
Solution Approach 2:
The patent implements a feedback mechanism that continuously compares current EA signal variations against reference values and triggers alerts when thresholds are exceeded. This feedback loop enables reliable long-term monitoring by automatically detecting and reporting changes, reducing the need for manual review and minimizing time investment while maintaining high assessment reliability.
3Measurement precision
If sleep apnea indicators are integrated into heart failure monitoring, then diagnostic accuracy is improved, but device complexity and measurement requirements increase
Solution Approach 1:
The patent makes the EA signal analysis system universal by enabling it to detect both heart failure status and sleep apnea indicators through a single integrated analysis framework. The same EA signal processing algorithms serve dual purposes: assessing cardiac function and detecting respiratory patterns, thereby improving diagnostic accuracy without proportionally increasing measurement complexity.
Solution Approach 2:
The patent uses the EA signal as an intermediary that indirectly reflects both cardiac and respiratory status. By analyzing variations in endocardial acceleration, the system can infer information about both heart failure and sleep apnea without requiring separate direct measurements, thus improving diagnostic accuracy while avoiding the complexity of multi-sensor integration.
Data Source
AI summary
This device includes a sensor of endocardiac acceleration EA and one or more circuits configured for: extracting from the EA signal a predetermined EA parameter, determining a period of sleep, evaluating the clinical condition of the patient based on the EA parameter variations on this sleep period, and issuing an alert of worsening of the patient's condition. The device further determines a variability index of the EA parameter on the sleep period, and then calculates a ratio between this calculated variability index and a reference value, and delivers this ratio as a clinical status index. The alert signal is generated by the crossing by this ratio of a predetermined alert threshold.