Computational Catheter Signal Modeler for Dynamic Body Systems
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current medical technologies lack accurate characterization of dynamic body systems, such as the cerebrospinal and cardiovascular systems, which limits the safe application of therapies and diagnostics, particularly in terms of fluid dynamics and volume changes, leading to conservative treatment approaches that may not be optimal.
Innovation Solution
A computational catheter device equipped with pressure sensors, external sensors, a signal modeler, and a signal analyzer that generates a calibration to predict and compare pressure signals, enabling safe navigation and intervention in dynamic body systems by analyzing pulsatile flows and fluid dynamics, and utilizing actuators for homeostasis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conservative treatment approaches are used to ensure patient safety, then patient safety is maintained, but therapeutic effectiveness is reduced
Solution Approach 1:
The system continuously monitors pressure signals from the dynamic body system and compares actual signals against predicted signals generated by the signal modeler. This feedback loop enables real-time adjustment of therapeutic parameters, allowing clinicians to safely increase treatment intensity while maintaining patient safety through continuous monitoring and automatic alerts when safety thresholds are approached.
Solution Approach 2:
The patent replaces conservative mechanical limits on fluid delivery with a computational model-based safety system. The signal modeler uses fundamental frequency analysis and system characterization to dynamically determine safe operating parameters, substituting static safety margins with adaptive, model-based safety boundaries that enable more effective therapy while maintaining safety.
2Measurement precision
If accurate characterization of dynamic body systems is implemented, then therapeutic precision is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary system characterization by analyzing the fundamental frequency of the dynamic body system before initiating therapy. The signal modeler pre-generates predicted pressure signals based on system characteristics, allowing the actual therapy delivery to proceed with real-time comparison rather than requiring complex real-time calculations during treatment. This preliminary modeling reduces computational complexity during active therapy.
Solution Approach 2:
The signal modeler creates a computational copy or model of the patient's dynamic body system based on initial measurements. This virtual model generates predicted pressure signals that can be compared against actual measurements, enabling accurate system characterization without requiring complex real-time computational analysis. The model copy serves as a simplified representation that captures essential system dynamics.
3Productivity
If larger volumes of intrathecal drugs are administered, then therapeutic effectiveness is improved, but risk of injury increases
Solution Approach 1:
The system transitions from static, conservative volume limits to dynamic, adaptive volume guidance based on real-time pressure monitoring and system characterization. The signal modeler continuously updates safe operating parameters based on the patient's specific system characteristics and real-time pressure responses, enabling larger drug volumes to be administered safely by dynamically adjusting delivery parameters rather than relying on fixed conservative limits.
Solution Approach 2:
The patent changes the parameter basis for safe drug delivery from fixed conservative volume limits to dynamic parameters derived from system characterization. By analyzing fundamental frequency, pressure signal patterns, and system compliance, the signal modeler generates patient-specific safe delivery parameters that allow larger drug volumes while maintaining safety margins tailored to each patient's unique physiology rather than applying universal conservative limits.
Data Source
AI summary
A computational catheter device including at least one lumen, pressure sensor(s), external sensor(s), a signal modeler, and a signal analyzer is provided. The pressure sensor generates a pressure signal for a dynamic body system, whereas the external sensor determines the fundamental frequency caused by cardiovascular pulsation or external oscillator. The signal modeler uses catheter location and the fundamental frequency to generate a predicted signal. This predicted signal may be compared to the actual pressure signal by the signal analyzer to generate a calibration. This calibration may include any of a measure of perivascular state, a waveform output which causes standing waves within the dynamic body system, a boundary condition for safe operations, and an indication of abnormal physiology for assistance in catheter navigation. The catheter device may also include an actuator which uses the calibration to achieve homeostasis by fluxing fluids and/or guiding catheter movements.