Image-Assisted Capacitive Sensor Selection for Bioelectrical Signal Quality
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Solution Overview
Problem
In differential voltage measurement systems for bioelectrical signals, particularly in medical imaging, achieving high signal quality is hindered by static discharge effects and patient movement, which complicates the selection and positioning of capacitive sensors, especially due to varying patient anatomy and dynamic physiological behavior.
Innovation Solution
A sensor selection facility that includes an image capture unit for acquiring patient image data, a position ascertainment unit to determine sensor electrode positions, an evaluation unit to assess signal quality, and a combination unit to define a strategy for combining sensor signals, allowing for real-time adaptation of sensor configurations to maintain signal quality during patient movement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If capacitive sensors are arranged in the immediate vicinity of the patient's body to achieve high signal quality, then measurement precision is improved, but device complexity increases due to the need for multiple sensors covering all possible ideal measurement points
Solution Approach 1:
The system dynamically changes the operational parameters of the capacitive sensor system by selectively activating different sensor combinations based on patient body characteristics detected through image data. This allows the system to adapt to varying patient anatomies without requiring all sensors to be permanently active, thereby maintaining high signal quality while reducing effective device complexity.
Solution Approach 2:
The system performs preliminary actions by acquiring patient image data and determining optimal sensor positions before actual bioelectrical signal measurement begins. This pre-positioning and pre-selection of sensors based on patient anatomy eliminates the need for trial-and-error sensor placement during the measurement process, resolving the contradiction between needing multiple sensors and maintaining simple operation.
2Measurement precision
If test measurements with individual sensors are carried out to identify favorably positioned sensors, then measurement precision is improved, but loss of time increases due to static discharge effects taking about 30 seconds to disappear
Solution Approach 1:
The system performs preliminary image acquisition and sensor position determination before actual signal measurement begins. By using image data to pre-identify optimal sensor positions based on patient anatomy, the system eliminates the need for time-consuming test measurements that would be affected by static discharge, thus saving time while maintaining measurement precision.
Solution Approach 2:
The system replaces physical trial-and-error sensor placement and testing with an image-based computational approach. Instead of physically moving sensors and performing test measurements (mechanical process), the system uses image processing and algorithms to determine optimal sensor positions, substituting a faster non-contact method that avoids static discharge issues entirely.
3Ease of operation
If analysis of signals at rest is performed to predict signal quality, then ease of operation is improved, but measurement precision deteriorates because it cannot predict signal quality during patient movements such as breathing
Solution Approach 1:
The system transitions from static signal analysis at rest to dynamic adaptation by continuously monitoring patient movement through image data and adjusting sensor configurations in real-time. This dynamic approach maintains ease of operation through automation while significantly improving measurement precision during patient movements such as breathing by adapting to changing conditions.
Solution Approach 2:
The system implements feedback by continuously acquiring patient image data during measurement and using this information to adjust sensor selection and configuration. This closed-loop feedback mechanism allows the system to maintain accurate signal quality prediction during patient movements by constantly adapting to the patient's current state, overcoming the limitations of static pre-measurement analysis.
4Measurement precision
If the heartbeat is used as the criterion for good signal quality, then measurement precision is improved, but loss of time increases because only a few sampled values are available at the start of an examination
Solution Approach 1:
The system performs preliminary determination of optimal sensor positions using image data before relying on heartbeat-based signal quality assessment. This allows the system to establish good signal quality criteria from the outset using anatomical information, rather than waiting to accumulate sufficient heartbeat samples, thus reducing the time loss at the beginning of the examination.
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 enhances bioelectrical signal quality, enabling more precise synchronization of medical imaging with cardiac motion and improving image quality by dynamically adjusting sensor configurations based on patient anatomy and movement.
Implementation Method 1
capacitive sensor electrodes for acquiring a measurement signal
Implementation Method 2
an image capture unit to acquire image data from a patient
Data Source
AI summary
A sensor selection facility is described. In an embodiment, the sensor selection facility includes an image capture unit for acquiring image data from a patient; a position ascertainment unit for ascertaining positions of the capacitive sensor electrodes relative to the body of the patient based upon the image data; an evaluation unit for ascertaining the anticipated quality of a sensor signal from the capacitive sensor electrodes based upon the ascertained positions; and a combination unit for defining a combination strategy for combining the sensor signals from the respective capacitive sensor electrodes based upon the ascertained signal quality of the capacitive sensor electrodes. A differential voltage measurement system is also described. A method and computer readable medium for adapting a differential voltage measurement system are moreover described.