Hemodynamic Data Reassembly with Sensor Priority Gap Filling
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Solution Overview
Problem
Existing hemodynamic monitoring systems face challenges in effectively integrating and interpreting data from multiple types of sensors, leading to overwhelming and incomplete assessments of patient hemodynamic parameters.
Innovation Solution
A method and system that prioritize and reassemble hemodynamic data from multiple sensors by assigning priority to datasets from invasive, minimally invasive, and non-invasive sensors, filling gaps in data sequences with values from higher-priority sensors, and generating a comprehensive, integrated data sequence for easier physician analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data from multiple sensors are integrated, then the completeness of hemodynamic assessment is improved, but the complexity of data processing increases
Solution Approach 1:
The patent segments the integration process into distinct hierarchical levels: primary data collection from multiple sensors, secondary prioritization and selection based on sensor reliability, and tertiary gap-filling operations. This segmentation transforms a complex simultaneous integration problem into a structured sequential process, reducing processing complexity while maintaining information completeness.
Solution Approach 2:
The system performs preliminary prioritization of sensor datasets before integration, establishing a hierarchy of reliability and clinical importance in advance. By pre-ranking sensors (e.g., invasive sensors prioritized over non-invasive), the system eliminates the need for complex real-time decision-making during data integration, simplifying the processing workflow.
2Loss of information
If data from multiple sensors are integrated, then the comprehensiveness of hemodynamic monitoring is improved, but the difficulty of data interpretation increases
Solution Approach 1:
The patent extracts and displays only the most clinically relevant hemodynamic parameters after multi-sensor integration, filtering out redundant or less important data. By presenting a curated subset of integrated data (e.g., prioritizing invasive sensor readings for critical parameters), the system maintains comprehensiveness while reducing interpretation difficulty for physicians.
Solution Approach 2:
The system introduces an intermediary processing layer that translates raw multi-sensor data into clinically meaningful interpretations. This intermediary layer performs automated gap-filling, conflict resolution, and parameter synthesis, presenting physicians with pre-interpreted results rather than raw multi-source data, thereby reducing interpretation difficulty.
3Reliability
If multiple sensors are used, then the reliability of hemodynamic data is improved, but the quantity of data to be processed increases
Solution Approach 1:
The patent applies partial action by selectively processing only the necessary portion of multi-sensor data based on pre-established priorities. Instead of equally processing all sensor inputs, the system focuses computational resources on high-priority sensors (e.g., invasive arterial pressure monitors), processing their data in full while using lower-priority sensors only to fill specific gaps, thus reducing overall data quantity processed while maintaining reliability.
4Duration of action of stationary object
If data gaps are filled using lower-priority sensor data, then the continuity of hemodynamic monitoring is improved, but the risk of data inaccuracy increases
Solution Approach 1:
The system prepares compensatory data sources in advance by maintaining a pre-ranked hierarchy of alternative sensors. When a data gap is detected, the system immediately activates the next available sensor in the priority sequence, having already validated its reliability characteristics. This beforehand preparation ensures continuity while minimizing accuracy risk, as the fallback sensors are pre-qualified.
Solution Approach 2:
The system implements feedback mechanisms that continuously monitor data quality and sensor performance. When lower-priority sensor data is used to fill gaps, the system tracks this usage and adjusts future prioritization based on observed accuracy. This feedback loop ensures that gap-filling operations maintain acceptable precision levels while preserving monitoring continuity.
Data Source
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AI summary
A method for monitoring a hemodynamic parameter of a patient includes sensing the hemodynamic parameter with a first sensor and communicating a first dataset of the hemodynamic parameter to a hemodynamic monitor throughout a monitoring period. A second sensor senses the hemodynamic parameter of the patient and communicates a second dataset of the hemodynamic parameter to the hemodynamic monitor throughout the monitoring period. The hemodynamic monitor saves the first dataset and the second dataset to a memory of the hemodynamic monitor. In a first step, a processor populates a data sequence with all of the values of the first dataset relative to time of the monitoring period. In a second step, the processor populates any segments of time missing a hemodynamic parameter value in the data sequence with a hemodynamic parameter value from a corresponding time segment in the second dataset after completion of the first step.