Detecting Local Tissue Latency in Cardiac Pacing Leads
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Solution Overview
Problem
Current cardiac pacing therapies face challenges in optimizing control parameters, such as atrio-ventricular (AV) and inter-ventricular (VV) delays, due to local tissue latency near pacing lead electrodes, which can lead to suboptimal heart synchronization and reduced therapeutic effectiveness.
Innovation Solution
An implantable medical device detects local tissue latency by analyzing timing relationships in electrogram signals and adjusts AV or VV delays to compensate for latency, using a processor to determine optimal pacing parameters based on fiducial points in the EGM signal, such as minimum and maximum times associated with minimum and maximum amplitudes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard CRT pacing therapy is delivered without detecting local tissue latency, then the pacing therapy can be administered, but the ventricular synchronization is suboptimal and therapeutic effectiveness is reduced
Solution Approach 1:
The system performs preliminary detection of local tissue latency by analyzing fiducial points on EGM signals before finalizing pacing parameter selection. This preliminary action allows the system to identify latency issues and adjust parameters in advance, ensuring optimal ventricular synchronization from the start of therapy.
Solution Approach 2:
The system continuously monitors EGM signals and uses feedback from fiducial point analysis to detect local tissue latency. Based on this feedback, the system automatically adjusts AV and VV delay parameters to compensate for detected latency, maintaining optimal therapeutic effectiveness throughout treatment.
2Ease of operation
If AV and VV delay parameters are manually optimized without considering local tissue latency, then the pacing therapy can be implemented, but the heart chamber synchrony is not maximized
Solution Approach 1:
The system performs self-optimization by automatically detecting local tissue latency through EGM signal analysis and adjusting AV and VV delay parameters without requiring manual intervention. This self-service capability ensures precise heart chamber synchrony while simplifying the operation for clinicians.
Solution Approach 2:
The system dynamically changes pacing parameters (AV delay and VV delay) based on detected local tissue latency characteristics. By automatically adjusting these parameters according to measured latency values, the system achieves precise ventricular synchronization tailored to each patient's specific tissue conduction properties.
3Reliability
If local tissue latency is detected and compensated for, then ventricular synchronization is improved, but the device complexity increases due to additional sensing and processing requirements
Solution Approach 1:
The system uses the existing EGM sensing capability of the pacemaker for dual purposes: standard pacing guidance and local tissue latency detection. By analyzing fiducial points on the same EGM signals already being monitored for pacing decisions, the system achieves improved ventricular synchronization without requiring separate dedicated sensing hardware.
Solution Approach 2:
The system replaces complex manual measurement and adjustment procedures with automated signal processing algorithms that analyze EGM fiducial points. This substitution of mechanical/manual processes with electronic detection and computational analysis achieves precise latency compensation while minimizing additional hardware complexity.
Data Source
AI summary
A system and method for identifying whether local tissue latency is present. The system and method comprises an implanted lead having a first electrode for cardiac pacing and sensing. A sensing module for sensing heart activity with the first electrode to produce an electrogram (EGM) waveform. A processor is configured to receive the EGM waveform and extract two or more features from the EGM waveform representative of heart activity in response to monoventricular or biventricular pacing stimulus at the electrode and identify local tissue latency at a site of the first electrode based upon at least two of the extracted features indicating local tissue latency.