Thoracic Electrode Patch With Personalized Bioimpedance Assessment
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Solution Overview
Problem
Existing bioimpedance measurement systems lack reproducibility and accuracy in assessing thoracic fluid levels and ventilation status across patients with diverse physiologic characteristics, particularly in critical care settings, and current imaging methods are costly and impractical for routine use.
Innovation Solution
A non-invasive multi-electrode patch applies broadband electrical stimuli to the thoracic region, using machine learning algorithms to personalize fluid and ventilation assessments based on patient-specific data, correcting for parasitic effects and modeling extravascular and intravascular compartments with a complex impedance model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional imaging methods (X-ray, echocardiogram, CT scan) are used to assess thoracic fluid levels, then diagnostic accuracy is improved, but cost and time consumption increase significantly
Solution Approach 1:
The patent replaces mechanical imaging systems (X-ray, CT scan, echocardiogram) with an electrical measurement system that uses bioimpedance spectroscopy. The system applies electrical signals at multiple frequencies through electrodes and analyzes tissue response to determine fluid levels, eliminating the need for expensive imaging equipment and radiologist interpretation while providing real-time measurements.
Solution Approach 2:
The system enables self-service monitoring by providing automated real-time fluid level assessment that can be performed at the patient's bedside without requiring radiologist interpretation. The device autonomously processes electrical signals, applies machine learning algorithms, and generates diagnostic outputs immediately, allowing continuous monitoring without manual intervention.
2Productivity
If bioimpedance measurement systems are used to assess thoracic fluid levels, then measurement speed is improved, but accuracy and reproducibility across diverse patients deteriorate
Solution Approach 1:
The patent applies bioimpedance spectroscopy by measuring tissue response at multiple frequency parameters (from 10 Hz to 1 MHz) rather than using a single frequency. This multi-frequency approach captures different tissue characteristics and allows the system to adapt to diverse patient physiologies, improving both speed and accuracy simultaneously.
Solution Approach 2:
The system incorporates machine learning algorithms that continuously learn from patient-specific responses and adjust measurements accordingly. The algorithms analyze patterns across multiple frequency responses and provide feedback to refine the fluid level assessment, improving accuracy for individual patients while maintaining rapid measurement capability.
3Measurement precision
If invasive monitoring methods (vascular catheterization) are used to monitor vital parameters, then measurement accuracy is improved, but patient comfort and risk profile worsen
Solution Approach 1:
The patent replaces invasive mechanical monitoring systems (vascular catheters, arterial lines) with non-invasive electrical sensing through skin electrodes. The system measures bioimpedance changes that reflect vital parameters without penetrating the skin or entering the bloodstream, eliminating infection risks, bleeding, and patient discomfort while maintaining continuous monitoring capability.
4Device complexity
If single-frequency bioimpedance measurement is used, then device complexity is reduced, but ability to differentiate fluid compartments and assess ventilation status deteriorates
Solution Approach 1:
The patent segments the frequency spectrum into multiple measurement bands (from 10 Hz to 1 MHz) to separately characterize different tissue compartments. By analyzing responses at different frequencies, the system can differentiate between intravascular, extravascular, and interstitial fluid compartments, as well as detect air trapping, providing comprehensive physiological information without requiring a completely separate system for each measurement type.
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
Enables quantitatively accurate and reproducible monitoring of fluid and ventilation status in real-time, providing personalized insights and proactive alerts for clinical intervention, improving patient outcomes in critical care.
Implementation Method 1
a non-invasive multi-electrode patch applies broadband electrical stimuli to the thoracic region, using machine learning algorithms to personalize fluid and ventilation assessments
Data Source
AI summary
Systems and methods are providing for detecting and monitoring thoracic fluid, air-trapping and ventilation assessment in real time, wherein data obtained from a non-invasive electrode patch is analyzed using analysis algorithms for an electrical equivalent model that have been personalized for a patient's physiologic characteristics, medical condition and/or historical medical information using machine learning trained on a dataset representative of a large and diverse patient population. The systems and methods provide a simple, real-time, highly sensitive and specific, non-invasive, bedside solution for fluid level assessment, checking for increased air trapping, and ventilation assessment. The described methods include a variety of use cases for the inventive system and methods.