Cerebral Autoregulation Monitoring via Multi-Bin Correlation Analysis
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Solution Overview
Problem
Current systems for monitoring cerebral autoregulation struggle with accuracy due to noise in physiological parameter measurements and lack of reliable metrics for determining autoregulation status, leading to potentially inaccurate or unreliable autoregulation status calculations.
Innovation Solution
The use of processing circuitry to determine a composite estimate of the limit of autoregulation by analyzing correlation coefficient values across multiple bins with varying bin parameters, such as width and separation distance, to improve the accuracy of autoregulation status assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If correlation coefficient values are analyzed using single bin parameters, then processing is simplified, but measurement precision and reliability of autoregulation status determination deteriorates
Solution Approach 1:
The patent divides the analysis into multiple bins with different parameters (width, separation distance) to segment the correlation coefficient values. Each bin provides a specific perspective on the data, and combining these segmented views through voting or averaging mechanisms improves the overall measurement precision without requiring excessive processing complexity.
Solution Approach 2:
The patent systematically varies bin parameters (width and separation distance) to create multiple analyses of the correlation coefficient values. By changing these parameters across different bins and aggregating the results, the system achieves higher measurement precision while maintaining manageable processing complexity through structured parameter variation.
2Reliability
If multiple bins with varying parameters are used to analyze correlation coefficients, then measurement precision and reliability improve, but device complexity and processing requirements increase
Solution Approach 1:
The patent segments the analysis into multiple bins with different parameters, where each bin independently processes a subset of correlation coefficient values. This segmentation allows parallel processing and distributes computational load, improving reliability through multiple independent analyses while managing overall processing complexity.
Solution Approach 2:
The patent merges the results from multiple bins with different parameters through voting mechanisms or averaging. This combining step aggregates the reliable information from each bin to produce a final autoregulation status determination, achieving high reliability while the structured merging process keeps processing complexity manageable.
3Measurement precision
If bin width is reduced to improve measurement precision, then autoregulation status determination accuracy improves, but processing time and computational load increase
Solution Approach 1:
The patent segments the data into multiple bins with different widths, including some bins with smaller widths for higher precision estimation. By having multiple bins with varying widths, the system can balance processing time and precision, using wider bins for quick estimates and narrower bins for precise measurements where needed.
Solution Approach 2:
The patent applies partial action by not all bins using the same narrow width. Instead, it uses a mix of bin widths where some bins provide sufficient precision without the full computational cost of uniformly narrow bins. This partial application of high-precision processing achieves adequate measurement precision while reducing overall processing time.
Data Source
AI summary
In some examples, a device includes processing circuitry configured to determine a set of correlation coefficient values for first and second physiological parameters. The processing circuitry is further configured to determine a metric of the correlation coefficient values for a first plurality of bins and for a second plurality of bins, wherein each bin of the first plurality has a first bin parameter and each bin of the second plurality of bins has a second bin parameter different than the first bin parameter. The processing circuitry is also configured to determine a composite estimate of a limit of autoregulation of the patient based on the metric for the first plurality of bins and the metric for the second plurality of bins. The processing circuitry is configured to determine an autoregulation status based on the composite estimate and output, for display via the display, an indication of the autoregulation status.