Adaptive Phrenic Nerve Stimulation Detection in Cardiac Pacing
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Solution Overview
Problem
Implanted cardiac stimulation systems face challenges in avoiding unintended phrenic nerve stimulation, which can occur due to the varying anatomical location of the phrenic nerve and the proximity of pacing electrodes to the nerve, leading to discomfort and requiring time-consuming reprogramming to adjust pacing configurations and vectors.
Innovation Solution
A system and method that utilize a cardiac pulse generator, sensor, and phrenic nerve stimulation detector to characterize patient-specific PS features using high energy output pacing, allowing for the detection of PS beats with a large signal-to-noise ratio, and subsequently using these features to identify optimal pacing vectors and thresholds that minimize phrenic nerve stimulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high energy output pacing is used to detect PS features, then the signal-to-noise ratio is improved, but the risk of inducing phrenic nerve stimulation increases
Solution Approach 1:
The system performs preliminary detection of PS features using high energy output pacing to establish patient-specific thresholds and characteristics before actual therapy delivery. This preliminary characterization allows the system to identify safe pacing parameters in advance, avoiding phrenic nerve stimulation during therapeutic pacing by using the detected features to guide parameter selection.
Solution Approach 2:
The system varies pacing energy output parameters to detect PS features at different levels, then uses this information to select optimal therapy parameters that maintain effective cardiac pacing while staying below the phrenic nerve stimulation threshold. The parameter detection and selection process transforms the harmful high-energy stimulus into useful diagnostic information.
2Object-affected harmful factors
If pacing energy output is reduced to minimize phrenic nerve stimulation, then the safety margin is improved, but the signal-to-noise ratio of PS detection deteriorates
Solution Approach 1:
The system performs preliminary detection of PS features using high energy output pacing to establish patient-specific thresholds and characteristics before actual therapy delivery. This preliminary characterization allows the system to identify safe pacing parameters in advance, avoiding phrenic nerve stimulation during therapeutic pacing by using the detected features to guide parameter selection.
Solution Approach 2:
The system uses the detected PS features and thresholds as feedback to automatically adjust and select pacing parameters that maintain cardiac effectiveness while staying below phrenic nerve stimulation levels. The feedback loop continuously refines parameter selection based on the characterized PS response, enabling safe low-energy therapeutic pacing.
3Object-affected harmful factors
If manual reprogramming of pacing configurations is performed to avoid phrenic nerve stimulation, then the safety margin is improved, but the time required for adjustment increases
Solution Approach 1:
The system automatically detects PS features, determines patient-specific thresholds, and selects optimal pacing parameters without requiring manual clinician intervention. The device performs self-characterization of the phrenic nerve response and self-adjusts therapy parameters, eliminating time-consuming manual reprogramming while ensuring safe pacing parameter selection.
Solution Approach 2:
The system uses the detected PS features and thresholds as feedback to automatically adjust and select pacing parameters that maintain cardiac effectiveness while staying below the phrenic nerve stimulation threshold. The feedback loop continuously refines parameter selection based on the characterized PS response, enabling safe low-energy therapeutic pacing.
Data Source
AI summary
An example of a system comprises a cardiac pulse generator configured to generate cardiac paces to pace the heart, a sensor configured to sense a physiological signal for use in detecting pace-induced phrenic nerve stimulation (PS), a storage, and a phrenic nerve stimulation detector. The storage is configured for use to store patient-specific PS features for PS beats with a desirably large signal-to-noise ratio. The phrenic nerve stimulation detector may be configured to detect PS features for the patient by analyzing a PS beat with a desirably large signal-to-noise ratio induced using a pacing pulse with a large energy output and store patient-specific PS features in the storage, and use the patient-specific PS features stored in the memory to detect PS beats when the heart is paced heart using cardiac pacing pulses with a smaller energy output.