Adaptive Neural Network for Spinal Cord Stimulation Pulse Control
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Solution Overview
Problem
Current spinal cord stimulation (SCS) systems face challenges such as limited efficacy due to lead migration, habituation, and the need for frequent clinical sessions to adjust stimulation parameters, which are time-consuming and prone to error, and are not effectively adaptable to individual patient changes in vital signs and pain perception.
Innovation Solution
An adaptive spinal cord stimulation system with an implantable pulse generator (IPG) that uses an adaptive neural network trained by telemetry data from vital signs and spinal cord position, allowing for automatic adjustment of stimulation parameters, including burst and recovery pulses, to maintain efficacy over time and reduce power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional SCS systems use fixed stimulation parameters programmed during clinical sessions, then the system structure is simple, but the adaptability to individual patient changes in vital signs and pain perception is poor
Solution Approach 1:
The patent implements dynamic adaptation by training a neural network model using patient-specific telemetry data (vital signs, activity levels, pain reports) to generate time-varying stimulation parameters. The system transitions from static fixed parameters to dynamic adaptive parameters that automatically adjust based on patient state, resolving the contradiction between adaptability and complexity through data-driven personalization.
Solution Approach 2:
The system employs self-service mechanisms where the neural network automatically adjusts stimulation parameters based on patient feedback and telemetry data without requiring continuous clinical intervention. The embedded processor autonomously processes patient data and modifies stimulation settings, enabling the system to serve itself in adapting to patient changes while reducing the need for external programming sessions.
2Measurement precision
If clinical sessions are conducted frequently to adjust stimulation parameters, then parameter accuracy improves, but time consumption and error probability increase
Solution Approach 1:
The system implements continuous feedback loops where patient telemetry data (vital signs, activity, pain reports) is collected via wireless communication and used to automatically adjust stimulation parameters. This closed-loop feedback mechanism maintains parameter accuracy by continuously adapting to patient changes without requiring repeated clinical sessions, thereby reducing time loss while preserving measurement precision.
Solution Approach 2:
The neural network model is trained in advance using accumulated patient data to predict optimal stimulation parameters for different patient states. This preliminary training action enables the system to proactively adjust parameters before clinical sessions are needed, maintaining accuracy through pre-computed adaptive strategies that reduce the frequency and duration of clinical interventions.
3Reliability
If lead migration and habituation are addressed through frequent adjustments, then stimulation efficacy is maintained, but the need for frequent clinical sessions increases
Solution Approach 1:
The system dynamically adjusts stimulation parameters to compensate for lead migration and habituation by continuously monitoring patient responses and telemetry data. The neural network learns to adapt parameters over time to maintain efficacy despite physiological changes or lead position shifts, reducing the need for frequent clinical re-adjustments while preserving reliable pain relief.
Solution Approach 2:
The embedded neural network autonomously compensates for lead migration and habituation by automatically modifying stimulation patterns based on patient feedback and physiological data. This self-adjusting capability maintains stimulation efficacy without requiring frequent clinical intervention, enabling the system to service itself in maintaining reliable performance over extended periods.
Data Source
AI summary
This disclosure provides an SCS system comprised of an adaptive IPG. The IPG selectively chooses certain burst pulse parameters according to a pattern of frequencies and amplitudes that are determined by a low power hardware circuit. Alternatively, the patterns of frequencies and amplitudes are dictated by a predetermined list of parameters from a table of limited length. The IPG also is capable of receiving and storing vital sign telemetry, and spinal cord position to train an adaptive neural network to produce paresthesia pulses. Training of the IPG takes place after closing. Retraining the neural network allows the system to adapt to habituation, lead migration, and physiological changes in the patient's condition to maintain efficacy of the system over time. Training the neural network is also accomplished outside the IPG by a separate processor, which serves to conserve battery power and facilitate frequent retraining.