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通过通过专门的机器学习模型检测频率后续响应来推进听觉处理
Fuh-Cherng Jeng1,2, Katie Matzdorf1, Kassy L Hickman1
1Communication Sciences and Disorders, Ohio University, Athens, OH, USA.
Perceptual and motor skills
|December 28, 2023
概括
这项研究开发了一个专门的机器学习 (ML) 模型,使用源分离非负矩阵分解 (SSNMF) 来改进频率跟踪响应 (FFR) 的检测. 基于SSNMF的ML模型在识别听觉处理信号方面表现出更高的准确性和效率.
科学领域:
- 听觉神经科学 听觉神经科学
- 医疗保健中的机器学习
- 信号处理 信号处理
背景情况:
- 频率跟踪响应 (FFR) 对于评估听觉处理至关重要.
- 由于背景噪音和有限的数据,检测FFR可能具有挑战性.
- 现有的方法可能需要大量的数据或与噪音信号作斗争.
研究的目的:
- 评估一种专门的机器学习 (ML) 模型,用于检测头皮记录的频率跟踪响应 (FFR).
- 为了利用源分离非负矩阵分解 (SSNMF) 算法来增强FFR检测.
- 为了评估模型在噪音条件下和不同数量的记录扫描时的性能.
主要方法:
- 通过调整SSNMF算法用于FFR检测,开发了一种混合ML模型.
- 招募了40名听力正常的成年人,并使用英语母音 /i/ 唤起了FFR.
- 在FFR存在和FFR缺席条件下训练模型,评估灵敏度,特异性和效率.
主要成果:
- 专门的SSNMF ML模型显示,随着记录扫描的增加,提高了灵敏度,特异性和效率.
- 在500次扫描时,灵敏度超过80%,从1000次扫描开始,灵敏度超过89%.
- 具体性和效率也随着扫描数量的增加而迅速改善.
结论:
- 专门的SSNMF ML模型对于检测FFR是实用且有效的,即使数据和背景噪声有限.
- 随着更多的记录扫描,该模型的性能显著提高,这表明其坚固性.
- 这些发现支持这种ML模型在FFR研究和临床声学中用于听觉处理评估的更广泛应用.
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