Blood Pressure Estimation Using Pulse Wave Reflection Features
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for estimating blood pressure using bio-signals, such as PPG signals, face challenges in accurately determining cardiac output (CO) and total peripheral resistance (TPR) due to variations in these parameters, leading to inaccuracies in blood pressure estimation, especially in non-clinical settings.
Innovation Solution
A method and apparatus that utilize a sensor to measure bio-signals, extract cardiac output (CO) and total peripheral resistance (TPR) features by analyzing propagation and reflection wave components, and apply a blood pressure estimation model to estimate blood pressure based on these features, adjusting for variations in CO using predefined weights and differential signal analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods use simple bio-signal analysis for blood pressure estimation, then the device complexity is low and ease of operation is high, but measurement precision and reliability are insufficient due to variations in CO and TPR parameters
Solution Approach 1:
The patent segments the pulse wave signal into distinct components (propagation wave and reflection waves) and extracts features from each component separately. This segmentation allows for more precise measurement of CO and TPR parameters by analyzing specific waveform characteristics rather than treating the signal as a whole, thereby improving blood pressure estimation accuracy while maintaining manageable processing complexity through structured analysis.
Solution Approach 2:
The patent implements dynamic adjustment of the blood pressure estimation model based on detected variations in CO and TPR parameters. When significant variations are detected, the system adapts its calculation approach to account for these changes, ensuring maintained accuracy under varying physiological conditions rather than relying on static calibration parameters.
2Reliability
If the system adapts to variations in CO and TPR parameters, then measurement precision improves, but device complexity increases due to conditional processing logic
Solution Approach 1:
The patent employs feedback mechanisms by continuously monitoring CO and TPR parameter variations and using this information to adjust the blood pressure estimation process. The system detects changes in waveform features and feeds this information back to modify subsequent calculations, ensuring reliable estimates even when physiological parameters vary from calibration conditions.
Solution Approach 2:
The patent changes processing parameters dynamically based on detected waveform characteristics. When CO or TPR variations exceed thresholds, the system adjusts its calculation parameters and model selection to match the current physiological state, thereby maintaining reliability without requiring completely complex alternative processing paths.
3Measurement precision
If multiple reflection wave components are combined to calculate TPR, then measurement precision improves, but device complexity increases due to additional processing steps
Solution Approach 1:
The patent segments the reflection wave portion of the pulse signal into distinct reflection wave components based on their temporal and amplitude characteristics. By identifying and separately analyzing multiple reflection waves that occur after the propagation wave, the system can more accurately calculate TPR by combining features from these segmented components rather than using a single aggregated value.
Solution Approach 2:
The patent merges features from multiple identified reflection wave components to calculate the TPR parameter. By combining amplitude and timing information from several reflection waves rather than relying on a single component, the system improves TPR measurement precision through aggregated data while using systematic combination rules to manage processing complexity.
Data Source
AI summary
An apparatus for estimating blood pressure may include a sensor configured to measure a bio-signal from an object; and a processor configured to: obtain a cardiac output (CO) feature based on the bio-signal; obtain a total peripheral resistance (TPR) feature by combining an amplitude of a propagation wave component and amplitudes of at least two reflection wave components of the bio-signal, in response to a variation of the CO feature at a blood pressure measurement time relative to a calibration time, being within a predetermined range; obtain the TPR feature by combining the amplitude of the propagation wave component and an amplitude of one reflection wave component of the at least two reflection wave components, in response to the variation of the CO feature not being within the predetermined range; and estimate blood pressure based on the CO feature and the TPR feature.