Multivariate Statistical Models for Arterial Pressure Decoupling Detection
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Solution Overview
Problem
Current methods for monitoring cardiovascular conditions, such as vascular decoupling, lack precision and accuracy in real-time detection using arterial pressure waveform data, often relying on invasive techniques and failing to differentiate between normal and hyperdynamic conditions effectively.
Innovation Solution
Development of multivariate statistical models based on arterial pressure waveform data from subjects with and without specific vascular conditions, utilizing parameters like pulse beats standard deviation, R-to-R interval, and pressure weighted moments to provide distinct output values for model output, enabling more accurate differentiation between conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multivariate statistical models are developed using arterial pressure waveform data from multiple subjects, then measurement precision and detection accuracy are improved, but device complexity and data processing requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-collecting arterial pressure waveform data from multiple subjects during training phases, pre-computing statistical parameters (mean, standard deviation, skewness, kurtosis), and pre-establishing multivariate statistical models before actual detection is needed. This allows the system to have complex analytical capabilities ready in advance, reducing real-time processing complexity while maintaining high detection accuracy.
Solution Approach 2:
The patent uses copying by creating simplified statistical representations (mean, standard deviation, skewness, kurtosis values) that capture the essential characteristics of complex arterial pressure waveforms. These statistical copies serve as surrogate data that preserve the diagnostic information needed for detection while being much simpler to process and analyze in real-time applications.
2Productivity
If real-time monitoring of cardiovascular parameters is implemented, then productivity and response time are improved, but use of energy and computational resources increase
Solution Approach 1:
The patent extracts only the most essential statistical features (mean, standard deviation, skewness, kurtosis) from the complete arterial pressure waveform data for real-time monitoring. By taking out and focusing on these key parameters rather than processing the entire waveform continuously, the system achieves rapid monitoring with significantly reduced computational energy requirements while maintaining detection sensitivity.
Solution Approach 2:
The patent applies partial action by performing complete multivariate statistical analysis on training data during setup phases, then using simplified statistical parameter comparisons during real-time monitoring. This allows the system to have thorough analytical capabilities when needed for model development, while using lighter, faster computations during continuous monitoring to conserve energy.
Data Source
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AI summary
Multivariate statistical models for the detection of vascular conditions, methods for creating such multivariate statistical models, and methods for the detection of vascular condition in a subject using the multivariate statistical models are described. The models are created based on arterial pressure waveform data from a first group of subjects that were experiencing a particular vascular condition and a second group of subjects that were not experiencing the same vascular condition. The multivariate statistical models are set up to provide different output values for each set of input data. Thus, when data from a subject under observation is input into the model, the relationship of the model output value to the established output values for the two groups upon which the model was established will indicate whether the subject is experiencing the vascular condition.