Cerebral Blood Flow Autoregulatory Pattern Evaluation
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Solution Overview
Problem
Existing medical equipment cannot accurately define the autoregulatory pattern of cerebral blood flow in patients, which distorts the prediction of cerebrovascular disorders.
Innovation Solution
An electronic device equipped with a processor and transceiver that receives data sets containing blood pressure and blood flow velocity, groups them by blood pressure ranges, performs linear regression to calculate indicators, and groups data sets to determine autoregulatory patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing medical equipment is used to analyze cerebral blood flow, then cerebral blood flow data can be obtained, but the autoregulatory pattern cannot be accurately defined leading to distorted predictions
Solution Approach 1:
The patent segments the cerebral blood flow data by dividing blood pressure into multiple ranges (e.g., hypotension, normotension, hypertension) and analyzing blood flow velocity patterns within each segment. This segmentation allows the system to identify distinct autoregulatory patterns (Type I, II, III) that correspond to different physiological states, thereby improving measurement precision and prediction reliability.
Solution Approach 2:
The patent changes the analysis parameters from simple blood flow velocity measurement to a composite assessment including blood pressure ranges, blood flow velocity, and calculated cerebrovascular resistance. By transforming the data into standardized indicators (first indicator: blood flow velocity slope; second indicator: cerebrovascular resistance slope), the system achieves accurate autoregulatory pattern classification that was not possible with existing equipment.
2Measurement precision
If blood pressure and blood flow velocity data are collected and processed through grouping and linear regression, then autoregulatory pattern can be accurately evaluated, but the system complexity increases
Solution Approach 1:
The patent replaces complex manual analysis methods with automated computational algorithms. The linear regression calculation and automated grouping based on blood pressure ranges are performed through software processing rather than manual medical analysis. This substitution maintains high measurement precision while reducing the operational complexity burden on the medical professional using the system.
Solution Approach 2:
The patent simplifies the complex relationship between blood pressure and blood flow velocity by transforming it into standardized indicators through linear regression. The first indicator (slope of blood flow velocity vs. blood pressure) and second indicator (cerebrovascular resistance) provide a simplified representation of autoregulatory function, making the complex physiological assessment more manageable and interpretable.
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 non-invasive evaluation of the autoregulatory pattern of cerebral blood flow in patients, improving the accuracy of cerebrovascular disorder predictions.
Implementation Method 1
communicatively connect to an ultrasonic instrument through the transceiver, and receive the blood flow velocity of the data point from the ultrasonic instrument
Data Source
AI summary
An electronic device and a method for evaluating an autoregulatory pattern of cerebral blood flow are provided. The method includes the following. Each data point of a first data set is grouped according to a plurality of blood pressure ranges to generate a plurality of data groups respectively corresponding to the plurality of blood pressure ranges. A plurality of average values of blood flow velocity of the plurality of data groups are calculated. A first linear regression operation is performed on the plurality of average values of blood flow velocity to generate a first regression line. A first slope of the first regression line is calculated to obtain a first indicator. A plurality of data sets including the first data set are grouped according to the first indicator to generate a plurality of autoregulatory pattern groups. It is determined that a second data set corresponds to one of the plurality of autoregulatory pattern groups. An autoregulatory pattern of the second data set is output.