Gradient Adjustment for Autoregulation Limit Determination
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Solution Overview
Problem
Current methods for determining cerebral autoregulation limits, such as the lower and upper limits of autoregulation, face challenges in accurately differentiating between intact and impaired regions due to noise and physiological complexities, leading to unreliable autoregulation status assessments.
Innovation Solution
The use of a gradient adjustment method to transform cerebral oxygen saturation and mean arterial pressure data, enabling the determination of more accurate correlation coefficients and autoregulation limits by polarizing COx data into distinct intact and impaired zones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional correlation methods are used to determine autoregulation limits, then the measurement process is simple, but the measurement precision is poor due to noise and physiological complexities
Solution Approach 1:
The patent applies gradient adjustment as a preliminary transformation to the physiological data before performing correlation analysis. By pre-processing the rSO2 and MAP data to remove trends and stationary components, the method prepares the data in an optimal state for subsequent correlation coefficient calculation, thereby improving measurement precision without adding complex real-time processing during the actual measurement
Solution Approach 2:
The patent introduces gradient-adjusted data as an intermediary representation between raw physiological data and correlation coefficients. This intermediate form separates the trend components from the fluctuation components, allowing the correlation analysis to focus purely on the dynamic relationship between variables, thus improving accuracy while maintaining manageable processing complexity
2Measurement precision
If gradient adjustment transformation is applied to polarize COx data, then the discrimination between autoregulation zones is enhanced, but the device complexity increases
Solution Approach 1:
The patent transforms the physiological parameters (rSO2 and MAP) by removing their gradient components, changing them from stationary or trending signals to fluctuation-only signals. This parameter transformation polarizes the data distribution, making the distinction between intact and impaired autoregulation zones more pronounced and easier to detect, thereby improving discrimination accuracy
Solution Approach 2:
The gradient adjustment is performed as a preliminary step before correlation analysis and zone classification. By pre-transforming the data to remove trends, the method simplifies the subsequent analysis steps and improves the effectiveness of the correlation coefficient in distinguishing autoregulation zones, without requiring complex real-time processing
Data Source
AI summary
In some examples, a device includes processing circuitry configured to receive first and second signals indicative of first and second physiological parameters and determine a trendline function based on values of first and second physiological parameters. The processing circuitry is further configured to determine transformed values of the first physiological parameter based on the trendline function. The processing circuitry is configured to determine correlation coefficient values for the transformed values of the first physiological parameter and the values of the second physiological parameter. The processing circuitry is further configured to determine a limit of autoregulation of the patient based on the correlation coefficient values. 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.