Auditory Brainstem Response Threshold Detection with Adaptive Averaging
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Solution Overview
Problem
The accuracy of auditory brainstem response (ABR) hearing threshold determination is highly dependent on subjective judgment and varies due to differing signal-to-noise ratios and waveforms, making it difficult to automate and requiring significant professional manpower, which is inadequate for clinical needs, especially in large-scale infant and young child hearing screenings.
Innovation Solution
An automatic test device and method using an adaptive average method that dynamically adjusts the number of level averaging iterations based on cross-correlation analysis to detect ABR signals, determining the hearing threshold through function fitting and interpolation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If subjective judgment by specialists is used to determine hearing threshold, then flexibility in handling varying signal-to-noise ratios is maintained, but accuracy depends on personal experience and professional manpower requirements increase
Solution Approach 1:
The patent replaces the mechanical system of subjective human judgment with an automated computer-based analysis system. The system uses objective algorithms including cross-correlation analysis, time-locked signal detection, and adaptive averaging to automatically determine hearing thresholds, eliminating dependency on specialist experience while maintaining or improving accuracy.
Solution Approach 2:
The patent dynamically adjusts analysis parameters such as the number of averaging iterations, signal-to-noise ratio thresholds, and time window settings based on the actual quality of recorded ABR data. This adaptive approach allows the system to handle varying signal qualities automatically, replacing the need for human judgment in adjusting parameters for different recording conditions.
2Measurement precision
If fixed number of averaging iterations is used in ABR analysis, then analysis process is simplified, but insufficient averaging reduces detection accuracy while excessive averaging increases processing time
Solution Approach 1:
The patent implements dynamic adjustment of the averaging iteration count based on real-time assessment of signal quality. The system monitors metrics such as signal-to-noise ratio and waveform consistency, automatically increasing or decreasing the number of iterations needed to achieve reliable detection. This replaces fixed iteration schemes with adaptive dynamic control, optimizing both accuracy and processing efficiency.
Solution Approach 2:
The system incorporates feedback mechanisms where the quality of averaged ABR waveforms is continuously evaluated during processing. Based on this feedback, the system determines whether additional averaging is necessary or if the current average is sufficient, automatically stopping when detection criteria are met. This feedback-driven approach prevents both insufficient and excessive averaging.
3Productivity
If manual review of ABR recordings is performed, then complex varying waveforms can be interpreted using expert knowledge, but the process cannot meet increasing clinical needs for large-scale screening
Solution Approach 1:
The patent replaces manual review operations with automated computer-based analysis that can process large volumes of ABR data rapidly. The system incorporates sophisticated algorithms for waveform recognition, noise filtering, and threshold determination that replicate and extend expert knowledge, enabling high-throughput screening while maintaining diagnostic quality.
Solution Approach 2:
The patent creates a universal automated analysis system that can handle various ABR recording conditions, waveform variations, and clinical scenarios through a single integrated platform. The system adapts to different signal qualities and recording setups automatically, providing consistent analysis across diverse clinical needs without requiring specialized manual review for each case.
Data Source
AI summary
An automatic test device and method for auditory brainstem response (ABR) collects an ABR dataset at a plurality of sound loudness levels, increases the times of level averaging by iteration based on an adaptive average method, and improves a signal-to-noise ratio until ABR signal detection conditions are met. Signal detection includes determining that the time lag between average curves obtained from the ABR dataset is within a specified range. Iteration is terminated when the ABR signal is detected or a maximum number of iterations is reached. A minimum loudness level required to detect the ABR signal is used as a hearing threshold. An accurate loudness level corresponding to the hearing threshold is obtained by function fitting on the number of iterations used at each loudness level and interpolation. The threshold detection can effectively reduce the number of times that an ABR recording needs to be acquired.