Evoked Neural Potential Detection Under Stimulation Artifacts
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Solution Overview
Problem
Existing medical devices face challenges in detecting neural potentials evoked by electrical stimulation due to their long evolution time and masking by stimulation artifacts, especially under limited bandwidth conditions, making it difficult to capture and analyze these responses effectively.
Innovation Solution
The use of variation measures, such as standard deviation and variance, in electrical signals to identify second-order neural potentials, allowing for closed-loop stimulation adjustments based on these variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional signal detection methods are used to capture neural potentials, then the detection process requires high bandwidth and long sampling time, but this results in the neural potentials being masked by stimulation artifacts and difficult to detect under limited bandwidth conditions
Solution Approach 1:
The patent extracts only the essential features of neural potentials by computing variance of signal amplitudes within specific time windows, rather than attempting to capture and process the entire neural potential waveform. This extraction approach isolates the key diagnostic information while eliminating the need for high-bandwidth sampling of the full signal, thereby resolving the contradiction between detection accuracy and bandwidth requirements
Solution Approach 2:
The patent applies preliminary signal processing by computing the variance of signal amplitudes before further analysis. This preliminary action transforms the raw signal into a variance metric that highlights neural potential evocation without requiring the subsequent processing of large amounts of raw data, thus reducing bandwidth requirements while maintaining detection capability
2Loss of information
If high bandwidth sampling is used to capture neural potentials evoked by electrical stimulation, then more complete signal information is obtained, but the stimulation artifacts mask the neural potentials making them difficult to detect
Solution Approach 1:
The patent converts the harmful effect of stimulation artifacts into a beneficial detection method by computing variance metrics. Since artifacts are consistent across trials and neural potentials introduce variability, the variance computation inherently filters out the artifacts while highlighting the neural response information, thus converting the artifact problem into a solution
Solution Approach 2:
The patent introduces variance computation as an intermediary processing step between signal acquisition and analysis. This intermediary transformation converts raw signals containing both artifacts and neural potentials into variance metrics that separate these components, allowing neural potential detection without direct confrontation with the artifact masking problem
3Reliability
If multiple doses of electrical stimulation are delivered to evoke neural potentials, then more data for analysis is obtained, but the detection process becomes more complex and time-consuming
Solution Approach 1:
The patent replaces complex mechanical signal processing with a simple computational variance metric. By substituting elaborate signal analysis mechanisms with a straightforward variance calculation, the system can process multiple stimulation doses quickly and reliably without the time-consuming complexity of traditional waveform analysis methods
Data Source
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AI summary
An example system includes a memory; and processing circuitry configured to: cause an implantable stimulation device to deliver a plurality of doses of electrical stimulation to a patient; receive, for each respective dose of the plurality of doses, a respective electrical signal of a plurality of electrical signals; and determine, based on a variation of the plurality of electrical signals, whether the plurality of doses of electrical stimulation evoked neural potentials in the patient.