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基于多滤波器和属性自动机的听觉脑干反应峰值识别

Peak identification of auditory brainstem responses with multi-filters and attributed automaton.

作者信息

Grönfors T

机构信息

Department of Computer Science, University of Turku, Finland.

出版信息

Comput Methods Programs Biomed. 1993 Jun;40(2):83-7. doi: 10.1016/0169-2607(93)90002-3.

Abstract

An attributed automaton, a special case of attribute grammar, is a flexible tool in pattern recognition. It allows the utilization of contextual information from previously analyzed patterns in the analysis of the current pattern, and offers the possibility of describing those structural characteristics of patterns which cannot be described by classic methods of syntactic pattern recognition. Auditory brainstem responses are routinely used in audiology and otoneurology. Many studies on using the spectral analysis of averaged auditory brainstem responses have described at least two frequency bands, corresponding to the slow and fast components. Selective non-recursive digital filters for each frequency band in the spectrum of the auditory brainstem response have revealed enhancement or attenuation of components, depending on the band. In this study, multi-filters and an attributed automaton were combined for the identification of peaks.

摘要

属性自动机是属性语法的一种特殊情况,是模式识别中的一种灵活工具。它允许在当前模式的分析中利用来自先前分析模式的上下文信息,并提供了描述模式的那些经典句法模式识别方法无法描述的结构特征的可能性。听觉脑干反应在听力学和耳神经学中经常使用。许多关于使用平均听觉脑干反应的频谱分析的研究已经描述了至少两个频段,分别对应于慢成分和快成分。针对听觉脑干反应频谱中的每个频段的选择性非递归数字滤波器已经揭示了成分的增强或衰减,这取决于频段。在本研究中,将多滤波器和属性自动机相结合用于峰值识别。

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