Ultrasonic Microbubble Detection in Extracorporeal Blood Circuits
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Solution Overview
Problem
Existing methods for monitoring air in extracorporeal blood circuits during treatments like hemodialysis are inadequate for detecting small air bubbles, known as microbubbles, which can lead to serious complications if not detected early.
Innovation Solution
A method and device that couple a sequence of signal pulses or a continuous signal into the flowing liquid, extracting a characteristic signal pattern and comparing it to a reference pattern to detect the presence of microbubbles, using statistical parameters and spectral analysis to differentiate between air-free and air-loaded conditions, triggering an alarm when predetermined limits are reached.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional ultrasonic air detectors are used to monitor air bubbles in extracorporeal blood circuits, then relatively large air bubbles can be detected, but microbubbles (small air bubbles with volume < 50 μl) cannot be reliably detected
Solution Approach 1:
The patent changes the evaluation parameters from simple signal amplitude thresholds to statistical parameters (mean, standard deviation, skewness, kurtosis) and spectral characteristics of the received ultrasonic signal. This allows detection of microbubbles by analyzing the distribution and frequency characteristics of signal variations over time, rather than relying on absolute signal levels that work only for large bubbles.
Solution Approach 2:
The patent uses excessive sampling (acquiring many signal samples over time) and applies statistical analysis to extract meaningful patterns from noisy data. By collecting excessive data points and analyzing their statistical properties, the system can distinguish microbubble signals from environmental noise and compensation signals, achieving reliable microbubble detection.
2Measurement precision
If signal thresholds are lowered to detect microbubbles, then detection sensitivity improves, but false alarms increase due to environmental influences
Solution Approach 1:
The system continuously monitors statistical parameters of the received signal and compares them against dynamically updated reference values. When deviations exceed predetermined thresholds, the system generates alarms. This feedback mechanism allows adaptive detection that distinguishes true microbubble signals from environmental variations, reducing false alarms while maintaining sensitivity.
Solution Approach 2:
The patent introduces statistical parameters (mean, standard deviation, skewness, kurtosis) as intermediary variables between the raw ultrasonic signal and the detection decision. These statistical measures act as mediators that filter out environmental noise and compensation effects, allowing reliable detection of microbubbles without false alarms from environmental influences.
3Stability of the object's composition
If compensation for environmental influences is applied over long periods, then measurement stability improves, but detection of small air bubbles becomes difficult due to signal averaging
Solution Approach 1:
The patent segments the signal analysis into different statistical components (mean, standard deviation, skewness, kurtosis) and different frequency components through spectral analysis. By analyzing multiple segments of the signal characteristics simultaneously, the system can compensate for environmental influences affecting the mean while detecting microbubbles through variations in higher-order statistical moments that are less affected by long-term drift.
Solution Approach 2:
The system performs periodic statistical analysis and spectral analysis at defined time intervals rather than continuous averaging. This periodic evaluation maintains measurement stability by resetting reference values periodically while preserving the ability to detect microbubbles through transient signal variations that occur between periodic updates.
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
This approach allows for the reliable detection of microbubbles with high sensitivity, reducing the risk of air infusion into patients by differentiating between various air entry scenarios and providing timely alarms to prevent complications.
Implementation Method 1
The known air detectors are based on the different absorption of ultrasound in liquid and gaseous media and the scattering of ultrasound at interfaces
Implementation Method 2
The known air detectors are based on the different absorption of ultrasound in liquid and gaseous media and the scattering of ultrasound at interfaces
Implementation Method 3
a signal pattern is extracted which is characteristic of the time course of the received signal pulses in a predetermined time interval
Data Source
Figure 1
Figure 2
Figure 3~5b
AI summary
The invention relates to a method and a device for monitoring a flowing medium, in particular the blood flowing in an extracorporeal blood circulation (I), for the presence of air, in particular microbubbles. The basic principle of the method and the device according to the invention is that a sequence of signal pulses or a continuous signal is injected into the flowing medium and the signal pulses or continuous signal leaving the flowing medium is/are received. For this purpose, the device preferably has an ultrasonic transmitter (19) and an ultrasonic receiver (20). To detect microbubbles, a signal pattern that is characteristic of the variation over time of the received signal pulses or the continuous signal in a predetermined period of time is extracted from the signal received. The characteristic signal pattern is compared with one or more characteristic reference patterns, the presence of air bubbles being concluded if the characteristic signal pattern deviates from the characteristic reference pattern by a predetermined amount. Statistical characteristic variables, in particular the variance, are preferably determined from the signal patterns and compared with one another.