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连续小波变换和高阶谱:在呼吸音分析和基于脑电图的疼痛特征描述中的组合潜力。

Continuous wavelet transform and higher-order spectrum: combinatory potentialities in breath sound analysis and electroencephalogram-based pain characterization.

机构信息

Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceDepartment of Electrical and Computer Engineering, Khalifa University of Science and Technology, PO Box 127788, Abu Dhabi, UAE

出版信息

Philos Trans A Math Phys Eng Sci. 2018 Aug 13;376(2126). doi: 10.1098/rsta.2017.0249.

Abstract

The combination of the continuous wavelet transform (CWT) with a higher-order spectrum (HOS) merges two worlds into one that conveys information regarding the non-stationarity, non-Gaussianity and nonlinearity of the systems and/or signals under examination. In the current work, the third-order spectrum (TOS), which is used to detect the nonlinearity and deviation from Gaussianity of two types of biomedical signals, that is, wheezes and electroencephalogram (EEG), is combined with the CWT to offer a time-scale representation of the examined signals. As a result, a CWT/TOS field is formed and a time axis is introduced, creating a time-bifrequency domain, which provides a new means for wheeze nonlinear analysis and dynamic EEG-based pain characterization. A detailed description and examples are provided and discussed to showcase the combinatory potential of CWT/TOS in the field of advanced signal processing.This article is part of the theme issue 'Redundancy rules: the continuous wavelet transform comes of age'.

摘要

连续小波变换 (CWT) 与高阶谱 (HOS) 的结合将两个世界融合在一起,传递了有关所研究系统和/或信号的非平稳性、非正态性和非线性的信息。在当前的工作中,三阶谱 (TOS) 用于检测两种类型的生物医学信号,即喘息声和脑电图 (EEG) 的非线性和偏离正态性,与 CWT 相结合为被检查的信号提供了时频表示。结果,形成了一个 CWT/TOS 场,并引入了一个时间轴,创建了一个时频域,为喘息声非线性分析和基于 EEG 的动态疼痛特征提供了新的手段。提供了详细的描述和示例进行讨论,展示了 CWT/TOS 在先进信号处理领域的组合潜力。本文是主题为“冗余规则:连续小波变换崭露头角”的一部分。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/daab/6048582/a82f748fb306/rsta20170249-g1.jpg

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