Implantable Pacing Configuration Optimization via Endocardial Acceleration
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Solution Overview
Problem
Current CRT devices face challenges in optimizing pacing configurations due to the complexity of selecting appropriate stimulation sites and adjusting parameters like AVD and VVD, leading to inefficiencies in hemodynamic status optimization, particularly in patients who do not respond to conventional therapy.
Innovation Solution
A system utilizing endocardial acceleration sensors to deliver and process EA signals, isolating EA1 and EA2 components, and calculating composite indexes from parameters like peak amplitude, duration, and systole duration to evaluate and optimize pacing configurations automatically and precisely.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple electrodes and pacing configurations are used to optimize hemodynamic status, then the precision of stimulation site selection is improved, but the device complexity increases
Solution Approach 1:
The system continuously monitors hemodynamic parameters (such as pressure differentials or flow rates) and uses this feedback to automatically adjust pacing configuration parameters. The processor compares measured hemodynamic status against target values and modifies pacing settings in real-time, eliminating the need for complex manual optimization while maintaining high precision.
Solution Approach 2:
The pacing device performs self-optimization by automatically selecting optimal pacing configurations based on real-time hemodynamic monitoring. The system autonomously adjusts interventricular delay, atrioventricular delay, and electrode selection without requiring practitioner intervention, thereby simplifying operation while maintaining precision.
2Adaptability or versatility
If manual adjustment of pacing parameters is used, then the adaptability to patient-specific conditions is improved, but the time required for optimization increases
Solution Approach 1:
The system replaces manual mechanical adjustment of pacing parameters with an automated computational system. The processor analyzes hemodynamic data and automatically determines optimal pacing configurations, substituting the time-consuming manual trial-and-error process with rapid computational optimization that maintains patient-specific adaptability.
Solution Approach 2:
The system dynamically changes multiple pacing parameters (interventricular delay, atrioventricular delay, electrode selection) based on real-time hemodynamic feedback. By continuously adjusting these parameters according to measured physiological responses, the system achieves rapid patient-specific optimization without manual intervention.
3Measurement precision
If echocardiography-based assessment is used to evaluate pacing configurations, then the measurement precision is improved, but the productivity decreases
Solution Approach 1:
The system introduces an intermediary automated processing system that continuously monitors hemodynamic parameters through implanted sensors and processors. This intermediary system provides real-time evaluation of pacing configuration effectiveness, replacing the need for repeated echocardiography assessments while maintaining measurement precision through continuous physiological monitoring.
Solution Approach 2:
The system enables continuous real-time monitoring and evaluation of pacing configuration effectiveness through implanted hemodynamic sensors. Unlike intermittent echocardiography, the system continuously tracks hemodynamic responses, allowing for immediate detection of suboptimal configurations and rapid adjustment, thereby dramatically improving optimization efficiency while maintaining precision.
Data Source
AI summary
An implantable medical device includes a sensor configured to generate an endocardial acceleration (EA) signal representative of activity of a patient's heart. The device further includes one or more circuits configured to identify within the EA signal at least one EA signal component corresponding to at least one peak of endocardial acceleration, and extract from the at least one EA signal component at least two characteristic parameters. The one or more circuits are further configured to generate a composite index based on a combination of the at least two characteristic parameters, determine a plurality of values of the composite index for a plurality of pacing configurations, and select a current pacing configuration from among the plurality of pacing configurations based on the plurality of values of the composite index.


