Automating classification of ShoeBOX audiograms

Clearwater Clinals鞋盒产品使测听测试能够在现场和音箱外完成。它已经被广泛采用，包括在工业环境中监测职业健康和安全方面的听证。一旦受试者在不同频率下的听力敏感度被量化，下一步就是对听力图进行分类，以确定任何检测到的听力损失的特征。平坦、倾斜、隆起和凹槽等分类进一步明确为对称/不对称、病变部位和听力损失的总体严重程度。自动化这种听力图分类是迈向全自动化测试和诊断听力学系统的第一步。该项目将利用机器学习和信号处理技术创建数据驱动的自动和半自动听力图分类系统，适合作为鞋盒系统的一部分进行部署。由于ShoeBOX的广泛部署，Clearwater Clinic现在拥有一个非机密听力图的大型数据集。将开发一种快速听力图注释系统，以便专家听力学家可以对这些听力图进行分类。这个丰富的黄金标准数据集将被挖掘出来，以开发一个智能自动分类系统，使用机器学习的方法，包括规则提取和其他方法。还将开发一个半自动分类系统，用户可以在其中查看建议的分类，并根据自己的专业知识和其他背景信息进行快速调整。最终交付成果将包括音频图形记录环境、半自动和全自动分类系统的工作软件原型，以及详细说明其开发和评估的技术文件。

Clearwater Clinicals ShoeBOX product enables audiometry testing to be completed in the field and outside of the sound booth. Ithas already been widely adopted, including monitoring hearing in the context of occupational health and safety in industrialsettings. Once a subjects hearing sensitivity has been quantified at various frequencies, the next step is to classify the audiogramin order to characterize any detected hearing loss. Classifications such as flat, sloping, rising and notched are further specified asbeing symmetric/asymmetric, the site of lesion, and the overall severity of hearing loss. Automating such audiogram classificationis the first step towards a fully automated testing and diagnostic audiology system. This project will leverage machine learning andsignal processing techniques to create data-driven automated and semi-automated audiogram classification systems, suitable fordeployment as part of the ShoeBOX system. Thanks to ShoeBOXs wide deployment, Clearwater Clinical now possesses a largedataset of unclassified audiograms. A rapid audiogram annotation system will be developed such that expert audiologists canclassify these audiograms. This rich gold standard dataset will be mined to develop an intelligent automated classification systemusing methods from machine learning including rule extraction and others. A semi-automated classification system will also bedeveloped, where users are able to view the recommended classification and make rapid adjustments based on their ownexpertise and additional contextual information. Final deliverables will include working software prototypes of the audiogramannotation environment, semi- and fully-automated classification systems, as well as technical documentation detailing theirdevelopment and evaluation..Ontario