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Portable ABR acquisition and automated classification system using pasteless electrodes.

机译:使用无糊电极的便携式ABR采集和自动分类系统。

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摘要

A portable Auditory Brainstem Response (ABR) acquisition and classification system was developed in this study. The portable system consisted of pasteless electrodes, an ultrahigh input impedance bioamplifier and band-pass filter, a data acquisition PCMCIA card, a portable computer and the custom software for ABR acquisition and recognition.; The portable battery-powered ABR recording system utilizing pasteless electrodes has several advantages over existing technologies, requiring no skin preparation and paste. Simultaneous recordings utilizing the pasteless electrode/amplifier system and a conventional ABR acquisition system were performed and the results were compared.; Two types of neural networks, Multilayer Perceptron (MLP) and Radial Basis Function (RBF), were designed for ABR classification and their performances were compared. MLP trained by the back propagation method was found to have better performance and was finally implemented into the automated classification system.; In contrast to most of the previous studies on ABR recognition utilizing neural networks, which use only post-stimulus data, this study used both pre-stimulus and post-stimulus ABR data. New feature extraction methods utilizing peak-to-peak amplitude in running windows and latency were also developed. A rule based module was applied to the output of the neural network and the final classification (Response, No Response and Indeterminate) was given. A noise rejection module using pre-stimulus data was implemented for screening data prior to neural network classification. A total of 21 ears from 16 subjects yielding 1014 recordings were acquired, using the portable system, and were used for the neural network's training and testing. Different neural network models were designed and compared. The final model with the best performance produced 2.8%, 2.2% and 12.8% false positive, false negative and indeterminate recognition rates respectively, and it was implemented into the portable system.
机译:在这项研究中开发了便携式听觉脑干反应(ABR)采集和分类系统。便携式系统包括无糊电极,超高输入阻抗生物放大器和带通滤波器,数据采集PCMCIA卡,便携式计算机以及用于ABR采集和识别的定制软件。利用无糊电极的便携式电池供电ABR记录系统具有优于现有技术的多个优点,无需准备皮肤和粘贴。使用无糊电极/放大器系统和常规ABR采集系统同时进行记录,并比较结果。设计了两种神经网络,多层感知器(MLP)和径向基函数(RBF)进行ABR分类,并比较了它们的性能。发现通过反向传播方法训练的MLP具有更好的性能,并最终实现到自动分类系统中。与大多数以前使用神经网络进行ABR识别的研究相反,后者仅使用刺激后的数据,该研究同时使用了刺激前和刺激后的ABR数据。还开发了利用运行窗口中峰峰值幅度和等待时间的新特征提取方法。将基于规则的模块应用于神经网络的输出,并给出最终分类(响应,无响应和不确定)。实施了使用刺激前数据的噪声抑制模块,用于在神经网络分类之前筛选数据。使用便携式系统采集了来自16位受试者的21只耳朵,产生了1014条录音,并将其用于神经网络的训练和测试。设计并比较了不同的神经网络模型。具有最佳性能的最终模型分别产生了2.8%,2.2%和12.8%的误报,误报和不确定的识别率,并将其实施到便携式系统中。

著录项

  • 作者

    Song, Yuying.;

  • 作者单位

    University of Miami.;

  • 授予单位 University of Miami.;
  • 学科 Engineering Biomedical.; Health Sciences Audiology.; Artificial Intelligence.
  • 学位 Ph.D.
  • 年度 2000
  • 页码 159 p.
  • 总页数 159
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 生物医学工程;耳科学、耳疾病;人工智能理论;
  • 关键词

  • 入库时间 2022-08-17 11:47:39

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