Pressure Sensor Drift Detection in Intravascular Blood Pumps
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Solution Overview
Problem
Intravascular blood pumps face challenges with sensor drift due to sensitivity to temperature and other factors, leading to inaccurate operational data and alarms, which conventional techniques fail to automatically detect, relying on manual user intervention.
Innovation Solution
Implement data-driven techniques to detect sensor drift in pressure sensors using random-phase approximation and statistical methods to analyze pressure signals, recalibrating reference values when drift is detected based on signature range exceeding a threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pressure sensors are used to monitor operational data in intravascular blood pumps, then measurement capability is improved, but sensor drift occurs due to temperature sensitivity and manufacturing variations
Solution Approach 1:
The system performs preliminary calibration of the pressure sensor during the manufacturing process to establish baseline characteristics. This preliminary action compensates for manufacturing variations in the silicone membrane thickness before the sensor is deployed, reducing drift characteristics without requiring complex real-time corrections during operation.
Solution Approach 2:
The system continuously monitors the pressure signal and compares it against expected physiological ranges and calibrated baseline values. When deviations indicating sensor drift are detected, the system generates alerts and can trigger recalibration routines, creating a closed-loop feedback mechanism that maintains measurement accuracy despite temperature changes and aging.
2Device complexity
If manual user intervention is used to detect and correct sensor drift, then system complexity is reduced, but productivity and response time deteriorate
Solution Approach 1:
The system automatically detects sensor drift by analyzing pressure signal characteristics and comparing them against calibrated baselines without requiring user intervention. The controller autonomously identifies drift conditions, generates appropriate alerts, and can initiate recalibration sequences, enabling the system to self-correct measurement accuracy issues while maintaining operational continuity.
3Device complexity
If sensor drift is not detected, then device complexity remains low, but loss of information and measurement accuracy deteriorate
Solution Approach 1:
The system replaces manual visual inspection and physical recalibration with automated electronic analysis of pressure signal characteristics. The controller uses software-based algorithms to detect drift patterns in the pressure data, substituting mechanical/user-based detection with electronic signal processing that continuously monitors and identifies sensor degradation without adding significant hardware complexity.
Data Source
AI summary
Methods and apparatus for detecting sensor drift associated with a pressure sensor of a heart pump are provided. The method includes receiving a pressure signal from a pressure sensor arranged on the heart pump, determining a signature within a time window of the pressure signal, updating a minimum signature value or a maximum signature value based, at least in part, on the signature within the time window, determining whether a range of the signature is greater than a threshold value, wherein the range is determined as a difference between the minimum signature value and the maximum signature value, and performing an action when it is determined that the range of the signature is greater than the threshold value.