Blood Coagulation Analyzer Noise Exclusion via Regression Segmentation
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Solution Overview
Problem
Blood coagulation analyzers face issues with inaccurate analysis due to abnormal data from air bubbles or noise in time series data, which affects the reliability of the results, and require modifications based on varying measurement principles and reagents.
Innovation Solution
A sample analyzer that divides time series data into segments, determines first regression lines, selects the line with the highest matching degree, sets an analysis target region, and uses a second regression line for analysis, excluding noise and improving data accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If an approximated curve is obtained from time series data including noise, then the noise can be removed using the approximated curve, but the approximated curve itself includes inaccuracy which adversely influences the reliability of the analysis result
Solution Approach 1:
The time series data is divided into multiple data segments, and separate regression lines are determined for each segment. This segmentation allows the system to identify and exclude portions of data containing noise while maintaining the overall trend analysis, thereby resolving the contradiction between noise removal and curve accuracy.
Solution Approach 2:
The invention extracts and identifies abnormal data portions by comparing each data segment's regression line with the overall time series data. By taking out the noisy segments from the analysis and using only the clean segments to determine the final regression line, the system achieves both noise removal and high measurement precision.
2Reliability
If conventional fitting procedures are applied to time series data, then noise can be removed, but modifications must be made to reduce the influence of abnormal data in accordance with the shape of the graph obtained from time series data
Solution Approach 1:
The invention dynamically adapts the analysis procedure by automatically detecting the shape characteristics of the time series data and adjusting the regression analysis accordingly. The system determines whether to use segment-based regression or other fitting methods based on the actual data characteristics, making the procedure flexible rather than rigid.
Solution Approach 2:
The system performs self-adjustment by automatically identifying abnormal data patterns and selecting appropriate analysis methods without requiring external intervention or complex configuration. The regression analysis adapts itself to the data shape, simplifying the overall procedure while maintaining high reliability.
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
This approach enables highly reliable analysis by accurately identifying and excluding noise, leading to more precise coagulation reaction state analysis and increased reliability of blood coagulation test results.
Implementation Method 1
a measurement unit configured to irradiate the measurement specimen with light to acquire time series data related to the light absorption
Data Source
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AI summary
A sample analyzer, e.g. a blood coagulation analyzer, includes: a preparation unit (11) configured to mix a sample with a reagent to prepare a measurement specimen; a measurement unit (12) configured to irradiate the measurement specimen with light to acquire optical time series data; and a controller (4) configured to divide the time series data acquired by the measurement unit into data segments, determine first regression lines respectively of the data segments, select the first regression line with the highest matching degree with the time series data, set as an analysis target region a region of the time series data matching with the selected first regression line among the time series data acquired by the measurement unit, determine a second regression line using the time series data included in the set analysis target region, and perform an analysis using the second regression line.