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Density estimation and wavelet thresholding via Bayesian methods: a wavelet probability band and related metrics to assess agitation and sedation in ICU patients

机译:通过贝叶斯方法进行密度估计和小波阈值化:一个小波概率带和相关指标来评估ICU患者的躁动和镇静作用

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摘要

A wave is usually defined as an oscillating function that is localized in both time and frequency. A wavelet is a “small wave”, which has its energy concentrated in time providing a tool for the analysis of transient, non-stationary, or time-varying phenomena. Wavelets have the ability to allow simultaneous time and frequency analysis via a flexible mathematical foundation. Wavelets are well suited to the analysis of transient signals in particular. The localizing property of wavelets allows a wavelet expansion of a transient component on an orthogonal basis to be modelled using a small number of wavelet coefficients using a low pass filter. This wavelet paradigm has been applied in a wide range of fields, such as signal processing, data compression and image analysis.
机译:通常将波定义为在时间和频率上都局限的振荡函数。小波是“小波”,其能量集中在时间上,为分析瞬态,非平稳或时变现象提供了一种工具。小波具有通过灵活的数学基础同时进行时间和频率分析的能力。小波尤其适合于瞬态信号分析。小波的定位特性允许使用少量的小波系数和低通滤波器对正交分量上的瞬态分量进行小波扩展建模。这种小波范式已被广泛应用于信号处理,数据压缩和图像分析等领域。

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