Cardiac Resynchronization Therapy Device Real-Time Delay Optimization
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Solution Overview
Problem
Current methods for optimizing Cardiac Resynchronization Therapy (CRT) stimulation parameters, such as atrioventricular delay (AVD) and interventricular delay (VVD), are cumbersome, costly, and require frequent hospital visits, as they rely on echocardiography and complex algorithms that are not suitable for real-time, automated adjustments within cardiac implants.
Innovation Solution
A novel technique that uses a hemodynamic surface characterized by AVD and VVD parameters, employing a digital PID controller to optimize these delays concurrently, allowing for rapid, automated, and simple adjustments compatible with existing cardiac implant hardware, enabling real-time optimization with minimal hardware and software requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If echocardiography is used to optimize CRT parameters, then measurement precision is improved, but device complexity and loss of time increase due to hospital visits and manual procedures
Solution Approach 1:
The implantable device automatically optimizes its own CRT parameters using embedded sensors and control algorithms, eliminating the need for external echocardiography procedures. The device self-adjusts AVD and VVD parameters based on real-time hemodynamic feedback, making the system self-sufficient for parameter optimization.
Solution Approach 2:
The system implements closed-loop feedback by continuously monitoring hemodynamic parameters (such as ventricular pressure or flow) and using this information to automatically adjust CRT stimulation parameters. The feedback mechanism enables real-time optimization without external intervention, resolving the contradiction between precision and time loss.
2Measurement precision
If multiple stimulation configurations are tested to determine optimal parameters, then measurement precision is improved, but device complexity and loss of time increase due to the large number of combinations
Solution Approach 1:
The optimization process is made dynamic and adaptive, allowing the system to efficiently navigate the parameter space by learning from real-time hemodynamic responses. Rather than exhaustively testing all combinations, the dynamic algorithm adapts its search based on observed trends, reducing complexity while maintaining precision.
Solution Approach 2:
The system systematically varies stimulation parameters (AVD, VVD) and observes hemodynamic responses to identify optimal settings. By controlling and monitoring parameter changes in a structured manner, the system achieves precise optimization without requiring complex procedures, as the parameter space is explored efficiently through controlled variations.
3Productivity
If automated optimization algorithms are implemented, then productivity is improved through real-time adjustments, but device complexity increases due to processing requirements
Solution Approach 1:
The complex computational tasks are extracted from the implantable device and performed externally, with only essential control functions remaining in the implant. The external system handles heavy processing for optimization algorithms, while the implant executes simplified control commands, reducing the processing burden on the medical device while maintaining high productivity.
Solution Approach 2:
Complex mechanical or computational systems are replaced with simpler electronic or software-based solutions within the implant. The device uses efficient embedded algorithms and sensor feedback mechanisms that require minimal processing power, substituting complex hardware with streamlined electronic control systems that achieve real-time optimization with reduced complexity.
Data Source
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AI summary
The device has an error signal generator block (34) generating atrioventricular delay (DAV) and inter-ventricular delay (DVV) error signals according to a hemodynamics signal delivered by a hemodynamics sensor i.e. peak endocardial acceleration (PEA) type sensor, where the error signals respectively represent difference between DAV and DVV current values and DAV and DVV optimal values. Proportional-integral-derivative type controllers (36, 46) receive the error signals in an inlet and deliver the DAV and DVV signals in an outlet.