Implantable Spinal Cord Stimulation Feedback Control
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Solution Overview
Problem
Conventional spinal cord stimulation (SCS) systems lack objective feedback for assessing efficacy, leading to potential overtreatment or undertreatment, and do not adapt to changes in patient response over time, especially when used for chronic pain management or emerging applications requiring physiological parameter-based control.
Innovation Solution
An implantable system that delivers neurostimulation and senses spinal cord nerve impulse signals, analyzing them to adjust neuromodulation parameters such as amplitude, frequency, and electrode configuration to improve pain reduction and antiarrhythmic effects, using a pacemaker or other CRM devices to quantify and adapt SCS therapy based on nerve impulse and heart rate variability parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional SCS systems deliver stimulation with fixed parameters, then the system structure remains simple, but the stimulation efficacy cannot be objectively assessed or adapted over time
Solution Approach 1:
The patent implements a feedback mechanism where neural signals are sensed from the spinal cord in response to stimulation, analyzed to determine efficacy (such as pain reduction indicators), and used to automatically adjust stimulation parameters. This closed-loop feedback system resolves the contradiction by enabling adaptive efficacy improvement while managing system complexity through automated control algorithms.
Solution Approach 2:
The system performs self-adjustment of stimulation parameters based on real-time analysis of neural signals and efficacy indicators. The implantable device autonomously modifies its own operation without requiring external intervention, thereby improving reliability through continuous optimization while avoiding the complexity of external control systems.
2Measurement precision
If SCS parameters are adjusted manually by clinician and patient, then the system remains simple to operate, but there are no clear guidelines on parameter adjustment and overtreatment or undertreatment occurs
Solution Approach 1:
The system provides objective feedback through analysis of neural signals that indicate stimulation efficacy, such as changes in neural firing patterns associated with pain reduction. This quantitative feedback replaces subjective patient reports and clinician guesswork, enabling precise efficacy assessment while the automated adjustment removes the burden of manual parameter tuning from users.
Solution Approach 2:
The patent replaces manual mechanical adjustment of parameters with automated electronic control based on neural signal analysis. The system uses algorithms to interpret neural data and automatically modify stimulation settings, substituting the manual adjustment process with an intelligent control system that provides precise, objective parameter optimization.
3Adaptability or versatility
If SCS systems lack objective feedback, then the device complexity remains low, but there is no way to objectively assess efficacy or adapt to changes in patient response
Solution Approach 1:
The system continuously monitors neural signals and analyzes efficacy indicators to detect changes in patient response over time. This feedback loop enables the system to adapt stimulation parameters dynamically, improving adaptability while the integrated nature of the implantable device manages the added complexity through unified system architecture.
Solution Approach 2:
The patent transforms the static, fixed-parameter SCS system into a dynamic system that continuously adapts to changing patient needs. Neural signal analysis provides real-time information about treatment efficacy, enabling the system to modify parameters on-demand and maintain optimal performance as patient conditions evolve.
4Loss of information
If neural signal sensing and analysis is implemented, then objective efficacy feedback is achieved, but the device complexity and energy consumption increase
Solution Approach 1:
The system implements neural signal sensing and analysis to provide objective efficacy feedback, resolving the information loss problem. To manage energy consumption, the system uses efficient signal processing algorithms, processes signals intermittently rather than continuously, and leverages the existing implantable device infrastructure to minimize additional power requirements.
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
The system provides objective feedback to optimize SCS efficacy by quantifying pain reduction and adapting therapy parameters, ensuring consistent pain management and improving antiarrhythmic effects, thus enhancing the overall effectiveness of SCS treatment.
Implementation Method 1
an SCS system may be implanted within the body to deliver electrical pulses to nerves along the spinal cord
Implementation Method 2
spinal cord nerve impulse signals are sensed in response to the neurostimulation
Data Source
AI summary
Techniques are provided for controlling spinal cord stimulation (SCS) or other forms of neurostimulation. In one example, SCS treatment is delivered to a patient and nerve impulse firing signals are sensed along the spinal cord following the SCS treatment. The nerve impulse signals are analyzed to determine whether the signals are associated with effective SCS and then the delivery of additional SCS is controlled to improve SCS efficacy. For example, the nerve impulse signals can be analyzed to determine whether the signals are consistent with a positive patient mood associated with pain mitigation and, if not, SCS control parameters are adjusted to improve the efficacy of the SCS in reducing pain. In other examples, heart rate variability (HRV) is also used to control SCS. Still further, adjustments may be made to SCS control parameters to improve antiarrhythmic or sympatholytic effects associated with SCS. Techniques employing baseline/target calibration procedures are also described.