Faculty of Electronic Engineering & Technology, Universiti Malaysia Perlis, 02600, Arau, Perlis, Malaysia.
Advanced Communication Engineering, Centre of Excellence (ACE), Universiti Malaysia Perlis, 01000, Kangar, Perlis, Malaysia.
Sci Rep. 2023 Feb 1;13(1):1854. doi: 10.1038/s41598-022-25411-y.
Audiograms are used to show the hearing capability of a person at different frequencies. The filter bank in a hearing aid is designed to match the shape of patients' audiograms. Configuring the hearing aid is done by modifying the designed filters' gains to match the patient's audiogram. There are few problems faced in achieving this objective successfully. There is a shortage in the number of audiologists; the filter bank hearing aid designs are complex; and, the hearing aid fitting process is tiring. In this work, a machine learning solution is introduced to classify the audiograms according to the shapes based on unsupervised spectral clustering. The features used to build the ML model are peculiar and describe the audiograms better. Different normalization methods are applied and studied statistically to improve the training data set. The proposed Machine Learning (ML) algorithm outperformed the current existing models, where, the accuracy, precision, recall, specificity, and F-score values are higher. The reason for the better performance is the use of multi-stage feature selection to describe the audiograms precisely. This work introduces a novel ML technique to classify audiograms according to the shape, which, can be integrated to the future and existing studies to change the existing practices in classifying audiograms.
听力图用于显示不同频率下一个人的听力能力。助听器中的滤波器组设计用于匹配患者听力图的形状。通过修改设计滤波器的增益来匹配患者的听力图来配置助听器。在成功实现这一目标时存在一些问题。听力学家的数量短缺；滤波器组助听器设计复杂；并且，助听器适配过程很繁琐。在这项工作中，引入了一种机器学习解决方案，根据基于无监督谱聚类的形状对听力图进行分类。用于构建机器学习模型的特征是特殊的，可以更好地描述听力图。应用并统计研究了不同的归一化方法，以改善训练数据集。与现有的模型相比，所提出的机器学习（ML）算法表现更好，其准确性、精度、召回率、特异性和 F 分数更高。性能提高的原因是使用多阶段特征选择来精确描述听力图。这项工作介绍了一种根据形状对听力图进行分类的新型机器学习技术，它可以集成到未来和现有的研究中，改变现有的听力图分类实践。
