Cardiovascular Parameter Calculation Using Regular Cardiac Cycle Segmentation
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Solution Overview
Problem
Existing methods for monitoring cardiovascular parameters like stroke volume variation (SVV), pulse pressure variation (PPV), and systolic pressure variation (SPV) are limited by the presence of irregular cardiac cycles, which can lead to inaccurate assessments of preload dependence and fluid responsiveness, especially in critical care situations.
Innovation Solution
A method that identifies regular and irregular cardiac cycles in an arterial blood pressure waveform dataset, replaces the waveform characteristics of irregular cycles with estimated values based on regular cycles, and calculates cardiovascular parameters using a modified dataset to improve accuracy and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If irregular cardiac cycles are included in cardiovascular parameter calculations, then more data points are available for analysis, but measurement precision deteriorates due to inaccurate assessments of preload dependence and fluid responsiveness
Solution Approach 1:
The patent segments cardiac cycles into two distinct categories: regular cardiac cycles and irregular cardiac cycles. This segmentation allows the system to selectively process only regular cycles for cardiovascular parameter calculations, thereby maintaining measurement precision while still utilizing a sufficient quantity of valid data points. The segmentation is achieved through detection algorithms that identify irregularities in cardiac cycle patterns and exclude them from parameter computations.
2Quantity of substance
If all available cardiac cycles are used for parameter determination, then data sufficiency is improved, but reliability deteriorates due to the distorting effect of irregular cycles on preload dependence assessment
Solution Approach 1:
The patent applies preliminary action by detecting and identifying irregular cardiac cycles before they can contaminate the cardiovascular parameter calculations. The system performs preliminary filtering of the cardiac cycle data, marking irregular cycles for exclusion prior to the actual computation of SVV, PPV, or SPV parameters. This preliminary identification ensures that only reliable, regular cardiac cycles contribute to the final parameter determination, thereby maintaining assessment reliability.
3Quantity of substance
If irregular cardiac cycles are replaced with estimated values, then data completeness is maintained, but measurement precision may be compromised by estimation errors
Solution Approach 1:
Rather than replacing irregular cardiac cycles with estimated values (which could introduce estimation errors), the patent takes out or extracts only the regular, reliable cardiac cycles from the dataset for parameter calculations. This extraction approach maintains data completeness in terms of using sufficient valid cycles while avoiding the introduction of potentially erroneous estimated values. The system achieves this by selectively including only regular cycles in the modified waveform dataset used for computing cardiovascular parameters.
Data Source
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AI summary
Methods for determining a cardiovascular parameter, e.g., a parameter reflecting fluid or volume responsiveness, using a modified waveform dataset are described. The waveform dataset corresponds to a signal, for example, from an arterial blood pressure, or any signal proportional to, or derived from the arterial pressure signal. These methods involve identifying individual cardiac cycles in the waveform dataset, measuring the waveform characteristics for the individual cycles, then determining if the individual cardiac cycles are regular cardiac cycles or irregular cardiac cycles. Once any irregular cardiac cycles are detected, a respiratory parameter is measured. Next, a modified waveform dataset containing the waveform characteristics of the regular cardiac cycles and the waveform characteristics of the irregular cardiac cycles is created wherein the waveform characteristics of the irregular cardiac cycles are replaced with estimated waveform characteristics. Finally, a cardiovascular parameter is determined using the modified waveform dataset.