Cerebral Autoregulation Monitoring via Dynamic Correlation Filtering
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Solution Overview
Problem
Existing systems for monitoring cerebral autoregulation struggle with accuracy due to rapid changes in physiological parameters, leading to unreliable estimates of autoregulation limits and impaired autoregulation status determination.
Innovation Solution
Processing circuitry is configured to select and update correlation coefficient values based on rapid changes in physiological parameters, using techniques such as threshold rate determination and window length adjustment to improve the accuracy of autoregulation limit estimates and status assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If correlation coefficient values are calculated continuously without filtering rapid changes, then the system responds quickly to physiological changes, but the measurement accuracy deteriorates due to unreliable data during rapid transitions
Solution Approach 1:
The system performs preliminary detection of rapid changes in physiological parameters before calculating correlation coefficients. By identifying periods of rapid change in advance, the system can exclude these periods from correlation analysis, preventing inaccurate coefficient calculation while maintaining continuous monitoring capability.
Solution Approach 2:
The system applies different processing quality to different time periods: during periods of rapid physiological change, correlation coefficient calculation is suspended or marked as unreliable, while during stable periods, full correlation analysis is performed. This local differentiation of processing quality ensures accuracy where needed without sacrificing overall response capability.
2Quantity of substance
If all correlation coefficient values are used to determine autoregulation limits, then the estimation is based on comprehensive data, but the reliability deteriorates due to inclusion of inaccurate values from rapid parameter changes
Solution Approach 1:
The system extracts and removes correlation coefficient values that were calculated during periods of rapid physiological parameter changes. By separating these unreliable values from the dataset, the system maintains comprehensive data utilization while eliminating the harmful impact of inaccurate measurements on autoregulation limit estimation.
Solution Approach 2:
The system introduces an intermediary mechanism (detection of rapid changes) that mediates between the raw correlation coefficient data and the final autoregulation limit estimation. This intermediary layer filters out unreliable data points, allowing the system to use comprehensive data while ensuring only reliable values contribute to the final estimate.
Data Source
AI summary
In some examples, a device includes processing circuitry configured to determine a set of correlation coefficient values for values of first and second physiological parameters. The processing circuitry is further configured to determine that the first physiological parameter changes rapidly in a particular time period. The processing circuitry is configured to select a correlation coefficient value associated with the particular time period and determine an updated value of the selected correlation coefficient value in response to determining that the first physiological parameter changes rapidly in a particular time period. The processing circuitry is further configured to determine an estimate of a limit of autoregulation of the patient based on the set of correlation coefficient values and the updated value. The processing circuitry is configured to determine an autoregulation status based on the estimate of the limit of autoregulation and output, for display, an indication of the autoregulation status.