Implantable Electrode Stimulation With Piecewise Neural Control
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Solution Overview
Problem
Existing neurological disease treatment devices, such as deep brain stimulation systems, struggle with maintaining effective and robust adaptive stimulation over time due to fluctuations in symptomatology and electrode-tissue interface degradation, necessitating a solution that compensates for these changes.
Innovation Solution
A device with an implantable electrode and processing unit that senses neural activity, adjusts stimulation parameters using a control logic based on neural signals, employing piecewise functions to account for both real-time fluctuations and long-term changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional deep brain stimulation is programmed with a predefined constant stimulation setting, then the device complexity is reduced and ease of operation is improved, but the adaptability to symptom fluctuations and disease progression deteriorates
Solution Approach 1:
The system automatically adjusts stimulation parameters by processing neural activity signals and determining optimal settings without requiring manual reprogramming by a physician. The processing module continuously monitors neural signals and autonomously tunes stimulation parameters to adapt to changing symptomatology and disease progression.
Solution Approach 2:
The system implements a closed-loop feedback mechanism where neural activity signals are continuously recorded, processed, and used to adjust stimulation parameters. The processing module analyzes the recorded neural signals and modifies stimulation settings based on the detected neural activity patterns, creating a responsive adaptive control system.
2Adaptability or versatility
If adaptive stimulation techniques are implemented to manage symptom fluctuations, then the adaptability to patient-specific conditions is improved, but the reliability over time deteriorates due to electrode-tissue interface degradation and signal instability
Solution Approach 1:
The system dynamically adjusts stimulation parameters based on real-time neural activity signals rather than using fixed predetermined settings. The processing module continuously monitors neural signals and modifies stimulation parameters on-the-fly to adapt to changing physiological conditions, making the system responsive to both short-term symptom fluctuations and long-term disease progression.
Solution Approach 2:
The system changes stimulation parameters (amplitude, pulse width, frequency) based on processed neural activity signals. The processing module analyzes neural signals and dynamically adjusts at least one stimulation parameter to optimize therapeutic effect while compensating for electrode-tissue interface degradation and disease progression over time.
3Device complexity
If a single control logic is used to adjust stimulation parameters, then the device complexity is reduced, but the measurement precision and tuning accuracy of neural signals deteriorates
Solution Approach 1:
The control logic is segmented into multiple independent modules: a stimulation module that generates stimulation signals, a recording module that records neural activity, and a processing module that processes signals and determines parameter adjustments. This segmentation allows each module to specialize in specific signal processing tasks, improving overall measurement precision and tuning accuracy while maintaining manageable system complexity.
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 self-initialization and self-tuning of the device, providing robust and long-lasting optimized stimulation by iteratively adjusting control parameters based on neural activity signals, addressing symptom fluctuations and electrode-tissue interface modifications.
Implementation Method 1
an implantable electrode configured to sense neural activity signals and apply electrical stimulation signals
Implementation Method 2
a stimulation module configured to generate a stimulation signal to be carried at the implantable electrode
Data Source
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Figure 3A~3B
Figure 3C~4A
AI summary
The present disclosure generally relates to the field of treatment of neurological disease, and in particular to devices and methods for treating neurological diseases based on adaptive stimulation and to methods for controlling such devices. In detail, the present disclosure generally relates to a device (10) for adaptive treatment of neurological diseases comprising an implantable electrode (11) configured to sense neural activity signals and apply electrical stimulation signals, and a processing and stimulation unit (14) connected to the implantable electrode (11), wherein the processing and stimulation unit at least comprises: a stimulation module (16) configured to generate a stimulation signal to be carried at the implantable electrode (11), the stimulation signal being characterized by at least one stimulation parameter; an acquisition module (20) configured to record neural activity signal records of neural activity signals sensed by the implantable electrode; and a processing module (18) configured to process the neural activity signal records recorded by the acquisition module (20) based on a first control logic (22) and to tune the at least one stimulation parameter (A) based on at least one neural activity signal record processed according to the first control logic (22), the first control logic being a function depending on at least one signal feature (Fi) of the neural activity signal records, being based on at least one control parameter (Cj) and being made of a plurality of different function pieces, wherein each function piece of the plurality of different function pieces is related to a respective range of the at least one signal feature (Fi), wherein the processing module (18) is configured to process the neural activity signal records recorded by the acquisition module (20) based on a second control logic (23) and to tune the at least one control parameter (Cj) of the first control logic (22) based on at least one neural activity signal record processed according to the second control logic (23).