Automated Neuromodulation via Machine Learning
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Solution Overview
Problem
Existing neuromodulation systems rely on continuous patient feedback for real-time adjustment of electrical stimulation, which is inefficient and dependent on patient input.
Innovation Solution
A method and system for automated neuromodulation through machine learning, where a pulse generator and lead are implanted, and a control unit processes patient feedback and sensor data to formulate an optimized neuromodulation protocol, allowing for automatic adjustment of stimulation parameters without continuous patient input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If continuous patient feedback is used to adjust electrical stimulation levels, then the stimulation can be tailored to patient needs, but the system becomes dependent on continuous patient input and is inefficient
Solution Approach 1:
The system performs self-adjustment of stimulation parameters by automatically processing patient feedback and sensor data through machine learning algorithms. The control unit autonomously modifies electrical stimulation levels without requiring continuous manual patient input, enabling the system to serve itself while maintaining adaptability to patient needs
Solution Approach 2:
The system implements a closed-loop feedback mechanism where patient responses and sensor data are continuously collected, processed through machine learning models, and used to automatically adjust stimulation parameters. This feedback loop enables the system to learn from patient responses and autonomously optimize treatment without continuous manual intervention
2Ease of operation
If manual operation by patient or healthcare provider is used, then the system is simple to operate, but it requires continuous real-time feedback and is inefficient
Solution Approach 1:
The patent replaces the mechanical/manual operation system with an automated intelligent system. Instead of requiring continuous manual adjustment by patients or healthcare providers, the system uses machine learning algorithms and sensor data processing to automatically control stimulation parameters, thereby eliminating the inefficiency of manual operation while preserving ease of use through automated decision-making
3Productivity
If automated neuromodulation through machine learning is implemented, then the system becomes more efficient and consistent, but the device complexity increases
Solution Approach 1:
The control unit is designed to perform multiple functions: it processes patient feedback, integrates sensor data from various sources (accelerometer, gyroscope, magnetometer), executes machine learning algorithms, and controls electrical stimulation output. By consolidating these diverse functions into a single multi-functional control unit, the system achieves high treatment efficiency without proportionally increasing overall device complexity
Data Source
AI summary
An automated neuromodulation system which uses patient feedback during a training phase in combination with machine learning to automatically provide optimal nerve stimulation parameters based on the patient's needs without patient input. The automated neuromodulation system generally includes a lead which is implanted in a patient for treatment of a wide range of conditions, such as chronic pain. The patient is provided with a remote control through which the patient may provide either positive or negative feedback relating to how the patient is responding to electrical stimulation in different situations. A control unit monitors feedback from the patient as well as information and data from the time of feedback, such as the patient's position, orientation, speed of movement, or other considerations. Using machine learning, the control unit may process the feedback and data to formulate an optimized neuromodulation protocol and criteria which may be automatically applied to the patient.