Physiologic Waveform Validation for Closed-Loop Pressure Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current blood pressure monitoring technologies struggle with inaccurate and inconsistent management of hypotension and hypertension due to slow titration of vasopressors, leading to potential adverse cardiovascular, renal, and neurological complications.
Innovation Solution
A method and system for assessing waveform reliability using machine learning algorithms to validate blood pressure measurements, incorporating features like pressure, time, and morphology, and controlling infusion rates of vasopressors or fluids to maintain stable blood pressure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vasopressor titration is performed manually by hand, then the system is simple to operate, but the blood pressure management becomes inaccurate and inconsistent
Solution Approach 1:
The closed-loop control system enables self-service by automatically adjusting vasopressor infusion rates based on real-time blood pressure waveform analysis. The system independently monitors blood pressure, validates waveform reliability, determines appropriate adjustments, and executes infusion rate changes without manual intervention, thereby achieving accurate and consistent blood pressure management while reducing human error
Solution Approach 2:
The system implements continuous feedback by monitoring blood pressure waveforms in real-time, validating their reliability through multiple criteria, comparing actual blood pressure against target ranges, and automatically adjusting vasopressor infusion rates based on this feedback loop. This closed-loop feedback mechanism ensures accurate and consistent blood pressure control by dynamically responding to physiological changes
2Speed
If vasopressor titration is performed slowly by hand, then the system avoids rapid changes, but the response time to correct hypotension or hypertension becomes delayed
Solution Approach 1:
The system applies dynamics by making vasopressor infusion rates adjustable and responsive to real-time physiological conditions. The closed-loop control continuously adapts infusion rates based on current blood pressure levels, allowing rapid correction when needed while maintaining stability during normal conditions. This dynamic adjustment capability enables both fast response to hypotension/hypertension and reliable blood pressure maintenance
Solution Approach 2:
Through continuous feedback monitoring of blood pressure waveforms, the system detects deviations from target ranges and automatically adjusts vasopressor infusion rates with appropriate speed. The feedback loop ensures that rapid corrections are made only when necessary while maintaining blood pressure stability during normal operation, resolving the contradiction between speed and reliability
3Measurement precision
If continuous waveform monitoring is implemented, then the measurement precision of blood pressure improves, but the complexity of validating waveform reliability increases
Solution Approach 1:
The waveform validation process is segmented into multiple independent criteria including signal quality assessment, physiological plausibility checks, artifact detection, and reliability scoring. Each criterion evaluates a specific aspect of waveform quality, and the combined results determine overall reliability. This segmentation makes the complex validation process more manageable and systematic
Solution Approach 2:
The system introduces an intermediary validation layer between raw waveform acquisition and clinical decision-making. This intermediary layer automatically assesses waveform reliability using multiple criteria and provides a reliability score that guides whether the waveform should be used for control decisions. This intermediary simplifies the complexity by providing a clear reliability indicator without requiring direct interpretation of complex validation parameters
Data Source
AI summary
Methods and systems to validated physiologic waveform reliability and uses thereof are provided. A number of embodiments describe methods to validate waveform reliability, including blood pressure waveforms, electrocardiogram waveforms, and/or any other physiological measurement producing a continuous waveform. Certain embodiments output reliability measurements to closed loop systems that can control infusion rates of cardioactive drugs or other fluids in order to regulate blood pressure, cardiac rate, cardiac contractility, and/or vasomotor tone. Further embodiments allow for waveform evaluators to validate waveform reliability based on at least one waveform feature using data collected from clinical monitors using machine learning algorithms.