PPG-Based Systemic Vascular Resistance Measurement With Gravity Correction
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Solution Overview
Problem
Existing methods for determining systemic vascular resistance (SVR) are inaccurate and lack continuous, reliable measurements, particularly in patients with cardiovascular conditions, due to calibration issues and gravitational effects on photoplethysmography (PPG) devices, leading to imprecise blood pressure readings and limited spatial vector measurements.
Innovation Solution
A calibrated PPG device combined with machine learning models is used to correct for gravitational forces on blood pressure measurements, integrating pulse transit time (PTT) and blood pressure data to calculate SVR, and augmented by sensor devices for background state detection to reduce noise and artifacts, enabling continuous and accurate SVR determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If a PPG device is used to determine SVR, then continuous non-invasive measurement is achieved, but measurement precision deteriorates due to calibration issues and gravitational effects
Solution Approach 1:
The patent applies parameter changes by using machine learning models to dynamically adjust and correct PPG measurements based on gravitational effects and calibration data. The system transforms raw PPG signals into accurate blood pressure readings by modifying measurement parameters through learned corrections, enabling both continuous operation and high precision.
Solution Approach 2:
The patent introduces machine learning models as intermediaries between the PPG device and the final SVR calculation. These models act as mediators that process raw PPG signals, apply gravitational corrections, and generate calibrated blood pressure readings, thereby resolving the contradiction between continuous measurement and measurement precision.
2Ease of operation
If traditional SVR measurement methods are used, then measurement simplicity is maintained, but reliability deteriorates due to lack of continuous monitoring capability
Solution Approach 1:
The patent makes the PPG device multi-functional by enabling it to perform both continuous blood pressure monitoring and accurate SVR determination through machine learning integration. This universal approach maintains the ease of operation of PPG devices while significantly improving reliability through advanced signal processing and correction algorithms.
3Device complexity
If PPG devices operate without gravitational correction, then device complexity is reduced, but measurement precision deteriorates under varying positional conditions
Solution Approach 1:
The patent replaces complex mechanical calibration systems with machine learning-based gravitational correction algorithms. Instead of using complex mechanical structures to compensate for gravitational effects, the system uses computational models that learn and correct for gravitational influences, maintaining device simplicity while improving measurement precision.
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 provides continuous, accurate SVR measurements, enhancing medical diagnosis and treatment efficacy by improving the precision of blood pressure readings and reducing artifacts, particularly in patients with cardiovascular conditions.
Implementation Method 1
receiving a systolic blood pressure (SBP) and a diastolic blood pressure (DBP) from a calibrated photoplethysmography (PPG) device
Data Source
AI summary
Methods, systems, and computer-readable medium for determining a systemic vascular resistance (SVR), by receiving a systolic blood pressure (SBP) and a diastolic blood pressure (DPB) from a calibrated photoplethysmography (PPG) device, receiving a pulse transit time (PTT) from the PPG device, determining a stroke volume based on the PTT, determining a mean arterial pressure (MAP) based on the SBP and the DBP, receiving a heart rate, determining a cardiac output based on the heart rate and the stroke volume, determining a first value based on a right atrial pressure (RAP) or central venous pressure (CVP) and the map, determining a second value based on the first value and the cardiac output, and determining a SVR based on the second value and a factor.