Machine Learning for Auditory Brainstem Analysis

用于听觉脑干分析的机器学习

• 批准号: 2899239
• 金额: --
• 依托单位: University of Southampton
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2899239
• 关键词: Machine; Learning; Auditory; Brainstem; Analysis

The Auditory Brainstem Response (ABR) is an evoked potential measurement which is used clinically in the objective estimation of hearing thresholds, particularly in newborns. It is typically interpreted by the visual inspection of a clinician which may be aided by objective statistical measures. Visual interpretation is subjective and is known to be variable between clinicians, potentially leading to incorrect clinical decision making.This project aims to use machine learning algorithms to help provide an objective measure of the presence or absence of a response which may be used to aid clinicians in their interpretation of the ABR. Designing an objective measure using machine learning techniques has the potential to aid clinicians in more accurately and reliably interpreting ABRs, which would improve clinical decision making and could be used to improve the sensitivity and specificity of hearing screening devices.It is proposed to collect ABR data from healthy individuals at a set range of sensation levels to train and test the machine learning algorithm. No stimulus recordings will allow the specificity of the model to be assessed and the module will also be testing on simulated data where we can be sure that a response is present in the data. Nested k-fold cross-validation will likely be used to evaluate the performance of the model during its construction and to select the best performing feature extraction and dimensionality reduction techniques. Different feature extraction and dimensionality reduction techniques such as principal component analysis and independent component analysis may help to optimise model performance. Bootstrap analysis will be performed to provide a p-value for any given output of the machine learning algorithm. The model's performance will be compared to that of traditional statistical confidence measures.

听觉脑干反应（ABR）是一种诱发电位测量，临床上用于客观估计听力阈值，特别是新生儿。它通常通过临床医生的视觉检查来解释，这可以通过客观的统计测量来辅助。视觉解释是主观的，并且已知在临床医生之间是可变的，可能导致不正确的临床决策。该项目旨在使用机器学习算法来帮助提供反应存在或不存在的客观测量，其可用于帮助临床医生解释ABR。使用机器学习技术设计客观的测量方法有可能帮助临床医生更准确和可靠地解释ABR，这将改善临床决策，并可用于提高听力筛查设备的灵敏度和特异性。建议在设定的感觉水平范围内收集健康个体的ABR数据，以训练和测试机器学习算法。没有刺激记录将允许评估模型的特异性，并且模块还将对模拟数据进行测试，我们可以确保数据中存在响应。嵌套k折交叉验证可能用于评估模型在构建过程中的性能，并选择性能最佳的特征提取和降维技术。不同的特征提取和降维技术，如主成分分析和独立成分分析，可以帮助优化模型的性能。将进行Bootstrap分析，为机器学习算法的任何给定输出提供p值。该模型的性能将与传统的统计置信度进行比较。