Tissue Filtering Network for Electromagnetic Stimulation Waveform Optimization
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Solution Overview
Problem
Current biophysical models for electromagnetic tissue stimulation ignore fundamental tissue filtering properties, leading to inaccuracies in predicting stimulation effects and posing safety and dosing challenges.
Innovation Solution
The method accounts for tissue filtering properties by analyzing filtering networks of capacitive, resistive, and inductive elements within tissues to optimize electromagnetic stimulation waveforms, integrating with membrane, cellular, tissue, network, and organism models to predict and control stimulation responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If biophysical models ignore tissue filtering properties, then the models are simpler to use, but the prediction accuracy of stimulation effects deteriorates
Solution Approach 1:
The patent incorporates tissue filtering properties by changing the model parameters to include frequency-dependent impedance characteristics. The stimulation model now accounts for capacitive, resistive, and inductive elements that filter the stimulation waveform, transforming it from a simple resistive model to a complex impedance-based model that accurately predicts tissue response across different frequencies.
Solution Approach 2:
The patent introduces tissue impedance characteristics as an intermediary element between the stimulation source and the tissue response. This intermediary filtering network (comprising capacitive, resistive, and inductive elements) modifies the stimulation waveform before it reaches the target tissue, allowing the model to accurately capture the transformation of the applied field into the effective stimulating field.
2Reliability
If tissue filtering properties are accounted for, then stimulation safety and dosing accuracy improve, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary characterization of tissue filtering properties by measuring or estimating the impedance spectrum of the target tissue before applying stimulation. This preliminary action includes determining the capacitive, resistive, and inductive components of the tissue, which are then used to pre-calculate the filtered stimulation waveform, reducing the need for complex real-time computations during actual stimulation delivery.
Solution Approach 2:
The patent segments the tissue impedance into distinct filtering components (capacitive elements representing cell membranes, resistive elements representing cytoplasm and extracellular fluid, and inductive elements representing tissue structure). This segmentation allows the complex tissue response to be modeled as a series of simpler RC and RL circuits, making the computational burden manageable while maintaining accuracy.
3Productivity
If stimulation waveforms are optimized based on filtering properties, then stimulation efficacy improves, but the waveform design becomes more complex
Solution Approach 1:
The patent utilizes periodic stimulation waveforms with frequencies selected to match the resonant or optimal response frequencies of the target tissue, as determined by the tissue's filtering characteristics. By applying periodic action at these optimized frequencies, the stimulation efficacy is maximized while the waveform design remains relatively simple, relying on frequency selection rather than complex time-varying patterns.
Solution Approach 2:
The patent employs dynamic waveform adjustment where the stimulation parameters (frequency, amplitude, pulse width) are adapted based on the real-time or pre-measured tissue filtering properties. This dynamic approach allows the waveform to be optimized for each specific tissue type and individual patient, improving efficacy while the complexity is managed through automated parameter selection algorithms.
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 enables precise optimization of stimulation waveforms, enhancing safety and efficacy by accurately predicting tissue responses to electromagnetic fields, thereby improving clinical outcomes in neural and muscular stimulation.
Implementation Method 1
tissues can form a filtering network of capacitive, resistive, and/or inductive elements which cannot be ignored, as fields in the tissues can be constrained by these tissue electromagnetic properties
Implementation Method 2
tissues can form a filtering network of capacitive, resistive, and/or inductive elements
Implementation Method 3
tissues can form a filtering network of capacitive, resistive, and/or inductive elements
Data Source
AI summary
The invention generally relates to methods of stimulating tissue based upon filtering properties of the tissue. In certain aspects, the invention provides methods for stimulating tissue that involve analyzing at least one filtering property of a region of at least one tissue, and providing a dose of energy to the at least one region of tissue based upon results of the analyzing step.