Hearing Aid Frequency Band Optimization via Neurogram Similarity
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Solution Overview
Problem
Conventional methods for determining the optimal frequency band for hearing aids in hearing loss patients are inefficient, leading to excessive calculation and power consumption when the frequency band is high, and inadequate compensation when it is low, as they rely on statistical methods that do not account for individual hearing loss diagnostics.
Innovation Solution
A parameter determination apparatus that uses a similarity determiner to compare the neurogram of a normal person with that of a hearing loss patient, based on an auditory model reflecting damage to the middle and inner ear, to determine an optimal frequency band by analyzing the change tendency of similarity with respect to the frequency band, thereby calibrating the hearing device for efficient operation and reduced power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the frequency band is increased to improve hearing loss compensation, then the compensation effect increases, but the calculation amount and power consumption increase excessively
Solution Approach 1:
The patent dynamically adjusts the frequency band parameter based on the similarity between patient and reference neurograms. When similarity is high, a lower frequency band suffices, reducing power consumption. When similarity is low, the frequency band is increased to improve compensation effect, thus optimizing the trade-off between compensation quality and energy usage.
Solution Approach 2:
The system transitions from static frequency band settings to dynamic adjustment based on real-time similarity assessment. The frequency band is adaptively changed according to the patient's specific hearing loss characteristics, allowing the system to optimize performance for each individual while minimizing unnecessary computational overhead.
2Reliability
If the frequency band is increased to improve hearing loss compensation, then the compensation effect increases, but the calculation amount becomes excessive reducing operating efficiency
Solution Approach 1:
The frequency band parameter is dynamically adjusted based on neurogram similarity assessment. By changing this key parameter according to patient-specific characteristics, the system achieves optimal compensation effect while minimizing unnecessary calculations, thus maintaining high operating efficiency.
Solution Approach 2:
Instead of always using a high frequency band (excessive action), the system applies partial action by using only the necessary frequency band width required for effective compensation. This is determined by the similarity metric, allowing the system to avoid unnecessary computational overhead while maintaining adequate compensation performance.
3Ease of operation
If statistical methods are used to determine frequency band, then the process is simple, but the method is not appropriate for patients with different hearing loss diagnostics
Solution Approach 1:
The system performs self-adjustment by automatically assessing neurogram similarity and determining the appropriate frequency band without requiring manual configuration. This self-service capability enables the system to adapt to individual patient characteristics while maintaining ease of operation, as the adaptation occurs automatically based on objective measurements.
Solution Approach 2:
The system uses feedback from neurogram comparison to dynamically adjust the frequency band setting. By continuously assessing the similarity between patient and reference neurograms, the system receives feedback about the appropriateness of current settings and automatically adjusts parameters to optimize performance for each individual patient.
Data Source
AI summary
An apparatus and method are provided to determine a parameter using an auditory model of a hearing loss patient. The parameter determination apparatus determines a similarity between a neurogram of a normal subject and a neurogram of a hearing loss patient, and determines an optimal frequency band for the hearing loss patient based on the similarity.