Regional Oximetry Sensor Positioning Verification
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Solution Overview
Problem
Current methods fail to reliably determine whether a regional oximetry sensor is properly positioned on a subject, especially in scenarios where conventional sensor-off techniques cannot distinguish between tissue and inanimate objects, such as during cardiac surgery.
Innovation Solution
A system and method that involve receiving light intensity signals from both near and far detectors of a regional oximetry sensor, determining metric values based on these signals, and using these metrics to assess whether the sensor is correctly positioned by comparing them to predefined sensor-on regions, potentially utilizing neural networks for combination and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional sensor-off techniques are used to determine sensor positioning, then the method is simple to implement, but it cannot reliably distinguish between tissue and inanimate objects
Solution Approach 1:
The patent segments the measurement process into multiple independent metric evaluations. Instead of relying on a single conventional technique, the system divides the assessment into multiple metric calculations (first metric from first detector, second metric from second detector) that can be independently evaluated and combined to make a final positioning determination
Solution Approach 2:
The patent transitions from conventional two-dimensional sensor-off/on assessment to a multi-dimensional evaluation space by introducing multiple detectors and multiple metric types. This dimensional expansion allows the system to differentiate between tissue and inanimate objects by analyzing relationships across multiple measurement dimensions simultaneously
2Measurement precision
If multiple detectors and metrics are used to improve positioning accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent implements multi-functionality by using multiple detectors that serve dual purposes: each detector contributes to multiple different metric calculations. The first detector and second detector both participate in determining their respective metrics, which are then used together to assess sensor positioning, maximizing the utility of each component
Solution Approach 2:
The system employs feedback mechanisms where the relationships between multiple metrics are continuously evaluated against expected patterns for proper sensor positioning. The processors analyze the interrelationships between first and second metrics to provide feedback on whether the sensor is correctly positioned, allowing for real-time positioning verification
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate determination of sensor positioning on tissue, differentiating between proper placement and incorrect placement, even in challenging conditions like cardiac surgery, by analyzing light intensity metrics and their relationships.
Implementation Method 1
receiving a first signal representative of an intensity of light at a first detector of the regional oximetry sensor, and receiving a second signal representative of an intensity of light at a second detector of the regional oximetry sensor
Data Source
AI summary
Methods and systems are presented for determining whether a regional oximetry sensor is properly positioned on a subject. First and second metric values may be determined based on respective first and second light signals. The first and second metric values and a relationship between the first and second metrics are used to determine whether the sensor is properly positioned on the subject. The first and second metrics may form a pair of metrics, and whether the sensor is properly positioned on the subject may be determined based on whether the pair of metrics falls within a sensor-on region. In some embodiments, a plurality of metrics may be determined based on a plurality of received physiological signals. The plurality of metrics may be combined, using, for example, a neural network, to determine whether the regional oximetry sensor is properly positioned on a subject.