Catheter Tissue Proximity Calibration Using Reinforcement Learning
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Solution Overview
Problem
Existing catheter systems face challenges in accurately determining tissue proximity due to differences in processing hardware and methods between main and auxiliary signal processing units, leading to inconsistent tissue proximity indicators (TPIs) for electrodes connected to these units.
Innovation Solution
Employing reinforcement learning, specifically Q-learning and Deep Q-learning, to derive tissue proximity indicators for electrodes connected only to the auxiliary signal processing unit based on TPIs computed by the main unit, using a processing device to train a reinforcement learning agent through exploitation and exploration phases, and calibrating TPIs for electrodes connected to both units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If electrodes are connected to auxiliary signal processing unit, then system complexity is reduced, but tissue proximity indication accuracy becomes inconsistent
Solution Approach 1:
A reinforcement learning agent serves as an intermediary between the auxiliary signal processing unit and the main unit, learning to translate TPIs from the auxiliary unit to match those of the main unit through exploration and exploitation phases
Solution Approach 2:
The system changes the parameters of TPIs computed by the auxiliary unit through reinforcement learning, adjusting them to align with the main unit's TPIs while maintaining the simpler auxiliary processing architecture
2Adaptability or versatility
If different processing methods are used between main and auxiliary units, then processing flexibility increases, but TPI consistency deteriorates
Solution Approach 1:
The reinforcement learning agent receives feedback in the form of rewards and penalties based on how well its translated TPIs match those from the main unit, continuously adjusting its translation policy to improve consistency
Solution Approach 2:
The system performs preliminary calibration during exploration phases where the reinforcement learning agent learns the relationship between auxiliary and main unit TPIs before actual ablation procedures begin
Data Source
AI summary
In one embodiment, a method to find tissue proximity indications includes inserting a catheter into a body part of a living subject such that electrodes of the catheter contact tissue at respective locations within the body part, receiving signals provided by the electrodes, selectively rewarding and penalizing a reinforcement learning agent over reinforcement learning exploration phases to learn at least one tissue proximity policy responsively to at least one of the received signals, applying the reinforcement learning agent in reinforcement learning exploitation phases to find respective tissue-proximity actions to be taken that maximize respective expected rewards responsively to the at least one tissue proximity policy, and providing respective derived tissue-proximity indications of proximity of a given one of the electrodes with the tissue responsively to the found respective tissue-proximity actions.