Biological State Evaluation Using Quartic HRV Gradient Analysis
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Solution Overview
Problem
Existing blood pressure estimation methods using biological signal detection sensors may inaccurately classify individuals as hypertensive or normotensive, necessitating improved detection accuracy.
Innovation Solution
A biological state evaluation device and method that analyzes biological signal data through a three-dimensional knitted fabric and microphone, employing a quartic function to determine gradients of heart rate variability, and correlates these gradients with blood pressure values using a database to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional blood pressure estimation methods using biological signal detection sensors are used, then the measurement is easy and non-restraining, but the detection accuracy is insufficient leading to erroneous classification
Solution Approach 1:
The patent transforms the analysis approach by changing from conventional spectral analysis parameters to fractal analysis parameters (alpha values, fractal dimensions) that better capture the complex dynamics of blood flow variability. This parameter transformation enables more accurate blood pressure estimation while maintaining the non-invasive measurement approach
Solution Approach 2:
The patent replaces traditional mechanical blood pressure measurement (cuff-based) with a field-based analysis approach using acoustic signal processing and fractal mathematics. This substitution eliminates the need for mechanical restraint while improving measurement accuracy through advanced signal characterization
2Productivity
If simple blood pressure estimation methods are used, then the measurement process is simple and quick, but the reliability of classification between hypertensive and normotensive is low
Solution Approach 1:
The patent performs preliminary processing of blood flow variability signals by extracting fractal characteristics and alpha values before final blood pressure estimation. This preliminary analysis of the signal's fractal structure prepares the data for more reliable classification while maintaining efficient processing
Solution Approach 2:
The patent uses multiple fractal parameters (different alpha values at different frequency bands, fractal dimensions) that provide feedback about different aspects of blood flow dynamics. This multi-parameter feedback approach improves classification reliability by cross-validating blood pressure estimates from multiple fractal characteristics
Data Source
AI summary
Through biological signal data analysis, heart rate variability information is found and represented on log-log axes, a quartic function is set for a waveform resulting from analysis of the information, and one of two types of gradient values obtained based on different criteria, i.e., a gradient (GI) between points of inflection and a gradient (GT) of a tangent at a point of inflection (a GI value and a GT value), is employed as a final analysis result, depending on the number of extreme values in the quartic function. A cardiac output influenced by two factors, i.e., nervous system regulation which influences a heart rate and an arterial blood pressure which influences a stroke volume, undergoes increases/decreases due to the respective two factors. However, owing to the employment of one of the GI value and the GT value, an increase/decrease in the influence of each of these two factors is reflected.