Multi-Sensor Infusion System Air Occlusion Detection
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Solution Overview
Problem
Current infusion systems rely on single-sensor based methods for detecting air and occlusions, which lead to false positives and negatives due to faulty sensor observations and are prone to errors from factors like dancing micro air bubbles, stuck fluid droplets, and variable pressures, resulting in unnecessary interruptions of therapy.
Innovation Solution
A multi-sensor system that integrates signals from air, force, and pressure sensors to accurately determine the presence of air and occlusions in the fluid delivery line, using algorithms to combine and qualify these signals for improved robustness and reliability, thereby reducing false alarms and enhancing the sensitivity and specificity of air detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single-sensor based methods are used for detecting air and occlusions, then device complexity is reduced, but measurement precision and reliability deteriorate due to false positives and negatives
Solution Approach 1:
The patent combines multiple sensor types (acoustic air sensor, force sensor, pressure sensor) into an integrated monitoring system. The processor fuses data from all sensors to detect air and occlusions, resolving the contradiction by merging sensing functions to improve detection accuracy while managing system complexity through unified processing.
Solution Approach 2:
The system employs multi-functional sensors that detect different parameters (acoustic signals, force, pressure) to identify air and occlusions. This universal approach allows a single integrated system to perform multiple detection functions, improving measurement precision without requiring separate dedicated systems for each detection type.
2Device complexity
If single-sensor based methods are used, then device complexity is reduced, but reliability deteriorates due to false alarms and missed detections
Solution Approach 1:
The system implements feedback mechanisms where the processor continuously monitors sensor data and adjusts detection algorithms based on patterns observed. The multi-sensor feedback loop cross-validates signals, reducing false alarms and missed detections by comparing consistent patterns across multiple sensor types before triggering alerts.
Solution Approach 2:
The system performs preliminary analysis of sensor signals using algorithms that evaluate multiple parameters before making detection decisions. By pre-processing and cross-checking data from multiple sensors before final determination, the system reduces false positives and negatives, improving reliability while maintaining manageable complexity.
3Measurement precision
If multi-sensor system is implemented, then measurement precision and reliability improve, but device complexity increases
Solution Approach 1:
The system segments the detection function into specialized sensor modules (acoustic, force, pressure) that can be independently optimized and calibrated. Each sensor type focuses on specific detection aspects, and the processor integrates these segmented functions through structured algorithms, improving measurement precision while managing complexity through modular architecture.
4Reliability
If multiple sensors are integrated, then false alarms are reduced, but loss of time increases due to processing multiple signals
Solution Approach 1:
The system implements periodic sampling and processing of sensor signals at optimized intervals. By processing signals periodically rather than continuously, and by using event-triggered updates when threshold violations occur, the system reduces false alarms through thorough multi-sensor validation while minimizing time loss through efficient periodic processing cycles.
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
The multi-sensor system significantly reduces false positive air detection, improves the accuracy of air and occlusion detection, and minimizes unnecessary interruptions in infusion therapy by providing a more reliable assessment of fluid composition and line occlusions.
Implementation Method 1
When fluid is present in the tube, propagation of the acoustic signal is efficient and produces a large electrical signal via the receiver circuit. On the other hand, the presence of air in the tube causes an acoustical open circuit which substantially attenuates the detected signal.
Data Source
AI summary
An infusion system for being operatively connected to a fluid delivery line and to an infusion container includes a pump, a plurality of different types of sensors connected to the pump or the fluid delivery line, at least one processor, and a memory. The plurality of different types of sensors are configured to indicate whether air is in the fluid delivery line. The memory includes programming code for execution by the at least one processor. The programming code is configured to, based on measurements taken by the plurality of different types of sensors, determine: whether there is air in the fluid delivery line; whether there is a partial occlusion or a total occlusion in the fluid delivery line; or a percentage of the air present in the fluid delivery line or the probability of the air being in the fluid delivery line.