Auditory Evoked Response Detection Instrument
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Solution Overview
Problem
Current methods for detecting evoked neural responses, such as auditory brainstem responses, are limited by their reliance on statistical parameters that may not accurately account for individual background noise, leading to less accurate and slower detections.
Innovation Solution
An instrument comprising a stimulus generator, output unit, recording unit, and analysis unit is provided. The analysis unit determines the probability that measured evoked responses are driven by background noise by calculating an F-value based on the variance of the average evoked response and the residual background noise, with estimated statistical degrees of freedom.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If statistical methods (F-test, Q-sample, Hotelling-T2) are used for objective detection of evoked responses, then detection automation is improved, but measurement precision deteriorates due to inability to account for individual background noise properties
Solution Approach 1:
The patent changes the parameters used in statistical detection by introducing individualized background noise characterization through multiple metrics (variance, kurtosis, skewness) rather than assuming uniform noise properties. This allows the detection algorithm to adapt to each subject's specific noise profile, improving measurement precision while maintaining automation.
Solution Approach 2:
The patent performs preliminary characterization of individual background noise properties before conducting the actual evoked response detection. By pre-calculating noise metrics from baseline recordings and using these to guide subsequent detection, the system achieves both automation and high precision without requiring manual intervention during the actual detection process.
2Measurement precision
If bootstrapping methods are used in Q-sample test to improve noise estimation accuracy, then measurement precision is improved, but productivity deteriorates due to computational expense
Solution Approach 1:
The patent applies partial bootstrapping by performing bootstrapping only on a subset of data or for a limited number of iterations to obtain sufficient noise characterization, rather than exhaustively bootstrapping the entire dataset. This partial application achieves adequate noise estimation accuracy while significantly reducing computational burden and improving detection speed.
Solution Approach 2:
The patent performs noise estimation and bootstrapping operations in advance during a baseline recording phase, before the actual evoked response measurement. This preliminary characterization allows the main detection process to proceed quickly using pre-computed noise models, separating the computationally intensive tasks from the time-critical detection phase.
3Productivity
If F-test is used for detection due to its efficiency and simplicity, then productivity is improved, but measurement precision deteriorates because it cannot account for individual background noise types
Solution Approach 1:
The patent creates a universal detection framework that incorporates multiple detection approaches (F-test, Q-sample, Hotelling-T2) and automatically selects or combines them based on the characteristics of the individual subject's background noise. This multi-functional system maintains the efficiency of simple methods while achieving the precision of complex methods by adapting to each case.
Solution Approach 2:
The patent modifies the F-test by introducing individualized noise parameters (variance, kurtosis, skewness) derived from each subject's baseline recordings. This parameter adaptation allows the simple and efficient F-test to account for individual noise properties, improving measurement precision without sacrificing the computational efficiency that makes F-test attractive.
Data Source
AI summary
The present application relates to an instrument for detecting evoked responses. The instrument comprises a stimulus generator configured to generate at least one stimulus or a plurality of consecutive stimuli according to a test protocol, at least one output unit comprising a transducer, the output unit being configured to receive said at least one stimulus or plurality of consecutive stimuli from said stimulus generator and to provide said at least one stimulus or plurality of consecutive stimuli the test subject, at least one recording unit comprising one or more sensors for measuring one or more evoked responses of the test subject, in response to said provided at least one stimulus or plurality of consecutive stimuli, an analysis unit configured to receive and analyse said measured one or more evoked responses, where said analysis unit is configured to determine a probability, p, of whether each of said responses is driven by an underlying background noise during operation of said instrument, and where the analysis unit is configured to determine said probability, p, based on an F-value of the measured one or more evoked responses determined as a ratio between a variance of the average one or more evoked responses and a variance of a residual background noise. The application further relates to a method of detecting evoked responses.