Neural Response System for Neurological Diagnosis
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Solution Overview
Problem
Current electrovestibulography systems face challenges in accurately isolating and interpreting neural responses due to high noise levels and muscle artifacts, making it difficult to diagnose neurological and neurodegenerative disorders effectively.
Innovation Solution
A neural response system that includes multiple filters for processing tilt response signals, a segmenter for dividing signals into time segments, and a neural event extractor to generate biomarker data, which is then used by a diagnostic tool to determine the presence of neurological conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple filters and processing stages are applied to extract neural responses, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the neural response signal into multiple time segments and applies different filtering and processing techniques to each segment. This allows targeted extraction of specific neural events (such as ABR waves) while reducing overall system complexity by processing only relevant portions of the signal with appropriate filters.
Solution Approach 2:
The patent extracts specific neural events and biomarkers from the complex neural response signal by identifying characteristic features and isolating them through pattern recognition algorithms. This extraction process separates the diagnostically relevant information from the noise and irrelevant signal components.
2Measurement precision
If signal processing is enhanced to reduce noise, then measurement precision is improved, but loss of information increases
Solution Approach 1:
The patent applies preprocessing steps such as bandpass filtering and artifact rejection before main analysis to remove obvious noise and artifacts. This preliminary action prevents noise from interfering with subsequent analysis while preserving the neural response information through careful selection of processing parameters.
Solution Approach 2:
The patent uses iterative processing where the output of one analysis stage feeds back into refining the processing parameters for the next stage. This feedback mechanism allows the system to adjust filtering and segmentation parameters based on the actual signal characteristics, maintaining measurement precision while minimizing information loss.
Data Source
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AI summary
A neural response system, including a plurality of filters for each receiving and filtering a plurality of tilt response signals obtained from a person; a segmenter for segmenting the filtered response signals into time segments; and a neural event extractor for performing a neural event extraction process on each of the time segments to obtain and generate biomarker data representing a plurality of biomarkers for each segment.