Adaptive Neurological Event Detector Threshold Control
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Solution Overview
Problem
Current neurostimulation systems face challenges in accurately detecting neurological events due to fixed detection thresholds that do not adapt to changing physiological conditions, leading to suboptimal detection rates and potential over-stimulation, which can result in inadequate or inappropriate therapy delivery.
Innovation Solution
A system and method for automatically adjusting detection thresholds based on a target detection rate, using sensors to monitor electrographic signals and comparing actual detection rates to minimize differences, allowing for dynamic adjustment of threshold values to optimize event detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fixed detection thresholds are used in neurological event detectors, then device complexity is reduced and ease of operation is improved, but detection precision and reliability deteriorate due to inability to adapt to changing physiological conditions
Solution Approach 1:
The patent implements automatic threshold adjustment through feedback mechanisms. The system continuously monitors detection rates and compares them to target rates, then automatically adjusts thresholds to minimize differences. This closed-loop feedback system maintains high detection precision without requiring complex manual programming or frequent clinician intervention.
Solution Approach 2:
The neurological event detector performs self-adjustment of detection thresholds without external intervention. The system autonomously monitors its own detection rate, compares it to target rates stored in memory, and automatically modifies thresholds to achieve optimal performance, eliminating the need for complex external programming.
2Reliability
If fixed detection thresholds are used, then device complexity is minimized, but detection reliability deteriorates due to physiological condition variations
Solution Approach 1:
The patent transforms static fixed thresholds into dynamic adaptive thresholds. The system continuously adjusts detection thresholds based on actual detection rates and target detection rates, allowing the detector to adapt to changing physiological conditions while maintaining reliable event detection without increasing inherent device complexity.
Solution Approach 2:
The system automatically modifies detection threshold parameters based on performance monitoring. By changing the threshold parameter dynamically according to the difference between actual and target detection rates, the system maintains high reliability across varying physiological conditions while using simple automated adjustment logic.
3Adaptability or versatility
If manually adjusted thresholds are used, then adaptability is improved, but loss of time increases due to frequent clinician intervention
Solution Approach 1:
The neurological event detector autonomously manages threshold adaptation without requiring clinician intervention. The system self-monitors detection rates, self-compares them to target rates stored in memory, and self-adjusts thresholds automatically, eliminating time loss associated with manual programming while maintaining high adaptability to physiological changes.
Solution Approach 2:
The system implements automatic feedback-driven threshold adjustment. By continuously monitoring detection performance and automatically modifying thresholds based on feedback from detection rate comparisons, the system achieves adaptability without requiring time-consuming manual clinician intervention.
4Productivity
If lower detection thresholds are used to increase detection rate, then productivity is improved, but harmful factors increase due to false positives and over-stimulation
Solution Approach 1:
The system uses feedback control to optimize the balance between detection rate and false positives. By monitoring actual detection rates and automatically adjusting thresholds to match target rates, the system achieves high productivity while minimizing harmful false positives through continuous performance monitoring and adaptive threshold modification.
Solution Approach 2:
The system dynamically changes detection threshold parameters based on performance feedback. By automatically adjusting the threshold parameter to achieve target detection rates, the system optimizes productivity while minimizing false positives through data-driven parameter optimization rather than fixed conservative settings.
Data Source
AI summary
Methods and systems for detecting neurological events and approximating a target detection rate are disclosed. A target detection rate may be identified for the neurological event. Electrographic signals incident on a neurological event detector may be monitored. Each signal may be compared to a threshold value for a parameter. As the threshold value varies, it has a predictable effect on a detection rate of the neurological event. A rate at which the electrographic signals exceed the threshold value may be measured and compared to the target detection rate. The threshold value may be adjusted to minimize the difference between the measured detection rate and the target detection rate.