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matlab使⽤经验模式分解emd对信号进⾏去噪:对于这个例⼦，考虑由具有明显频率变化的正弦波组成的⾮平稳连续信号。
⼿提钻的振动或烟花声是⾮平稳连续信号的例⼦。
以采样频率加载⾮平稳信号数据fs，并可视化混合正弦信号。
1.load（'sinusoidalSignalExampleData.mat'，'X'，'fs'）;2.3.xlabel（'Time（s）'）;观察到混合信号包含具有不同幅度和频率值的正弦波。
为了创建希尔伯特谱图，您需要信号的IMF。
执⾏经验模式分解以计算信号的固有模式函数和残差。
由于信号不平滑，请指定' pchip'作为Interpolation⽅法。
[imf，residual，info] = emd（X，'Interpolation'，'pchip'）;1.⽬前的IMF | #Sift Iter | 相对Tol | 停⽌标准命中2.1 |2 | 0.026352 | SiftMaxRelativeTolerance3.2 | 2 | 0.0039573 | SiftMaxRelativeTolerance4.3 | 1 | 0.024838 | SiftMaxRelativeTolerance5.4 | 2 | 0.05929 | SiftMaxRelativeTolerance6.5 | 2 | 0.11317 | SiftMaxRelativeTolerance7.6 | 2 | 0.12599 | SiftMaxRelativeTolerance8.7 | 2 | 0.13802 | SiftMaxRelativeTolerance9.8 | 3 | 0.15937 | SiftMaxRelativeTolerance10.9 | 2 | 0.15923 | SiftMaxRelativeTolerance11.分解停⽌是因为残留信号的极值数⼩于'MaxNumExtrema'。
基于经验模式分解的心肺耦合技术在睡眠分析中的应用刘冬冬;张玲;杨晓文;张博;武文芳【摘要】Objective To explore application of the EMD (Empirical Mode Decomposition)-based CPC (Cardio-Pulmonary Coupling) technique in sleep analysis. Methods Through analysis of 30 cases of thoracic ECG signals recorded by PSG (Poly-Somno-Graphy), the instantaneous frequency and instantaneous phase were obtained with deployment of EMD so as to construct a CPC map. Then, CAP (Cyclic Alternating Pattern) was utilized to divide sleep into three stages: CAP Stage, Non-CAP Stage and Wake/REM (Rapid Eyes Movement) Stage. The waving degree of the maximum CPC peak was measured by ZCR (Zero Crossing Rate), which reflected the severity of OSAHS (Obstructive Sleep Apnea-Hypopnea Syndrome). Results The frequency band of OSAHS patients’ map was distributed centralizedly in the low-frequency areas with small waving changes of the maximum peak at each time. Comparisons were made between manual staging and automatic staging, which revealed that EMD-based CPC could differentiate accurately between the different sleep statuses. Significant differences existed between the waving principles of the maximum peak in OSAHS Patient Group and Healthy Volunteer Group. ZCR values were significantly different between Slight/Middle OSAHS Patient Group and Healthy Volunteer Group (P<0.001), and between Slight/Middle OSAHS Patient Group and Severe OSAHS Patient Group (P<0.001). Therefore, the maximum coupling peak value and apnea-hypopnea Index could be used as indexes to identify the different severity of OSAHS patients. Moreover, strong negative correlation was seen between the two indexes (r=-0.77,P=5.8×10-18).Conclusion Combinationof EMD and the CPC technique had proven its easy-to-operate features in data acquisition so as to provide reliable micro-structure and disorder information of sleep, which had huge development potentials in the ifelds of wearable health management and clinically-aided diagnosis.%目的：探讨基于经验模式分解的心肺耦合技术在睡眠分析中的应用。
经验模式分解（Empirical Mode Decomposition, EMD）是一种用于处理非线性、非平稳信号的适应性数据分析方法。
它可以将一个复杂的信号分解为一系列固有模态函数（Intrinsic Mode Functions, IMFs）。
在C语言中实现EMD算法需要一些数学和信号处理的基础知识。
以下是一个简单的EMD算法的C语言实现步骤：定义信号和分解的IMFs数量。
初始化残差和IMFs数组。
对信号进行经验模式分解，得到IMFs数组。
将IMFs数组中的元素相加得到残差。
将残差与原始信号进行比较，如果残差小于某个阈值，则停止分解；否则返回步骤3继续分解。
咨询QQ：3025393450有问题百度搜索“”就可以了欢迎登陆官网：/datablogmatlab使用经验模式分解emd 对信号进行去噪数据分析报告来源：大数据部落| 有问题百度一下“”就可以了对于这个例子，考虑由具有明显频率变化的正弦波组成的非平稳连续信号。
手提钻的振动或烟花声是非平稳连续信号的例子。
以采样频率加载非平稳信号数据fs，并可视化混合正弦信号。
load（'sinusoidalSignalExampleData.mat'，'X'，'fs'）;xlabel（'Time（s）'）;咨询QQ：3025393450有问题百度搜索“”就可以了欢迎登陆官网：/datablog观察到混合信号包含具有不同幅度和频率值的正弦波。
为了创建希尔伯特谱图，您需要信号的IMF。
执行经验模式分解以计算信号的固有模式函数和残差。
由于信号不平滑，请指定' pchip'作为Interpolation方法。
[imf，residual，info] = emd（X，'Interpolation'，'pchip'）;目前的IMF | #Sift Iter | 相对Tol | 停止标准命中1 |2 | 0.026352 | SiftMaxRelativeTolerance2 | 2 | 0.0039573 | SiftMaxRelativeTolerance3 | 1 | 0.024838 | SiftMaxRelativeTolerance4 | 2 | 0.05929 | SiftMaxRelativeTolerance5 | 2 | 0.11317 | SiftMaxRelativeTolerance6 | 2 | 0.12599 | SiftMaxRelativeTolerance7 | 2 | 0.13802 | SiftMaxRelativeTolerance咨询QQ：3025393450有问题百度搜索“”就可以了欢迎登陆官网：/datablog8 | 3 | 0.15937 | SiftMaxRelativeTolerance9 | 2 | 0.15923 | SiftMaxRelativeTolerance分解停止是因为残留信号的极值数小于'MaxNumExtrema'。
经验模式分解摘要近些年来，随着计算机技术的高速发展与信号处理技术的不断提高，人们对图像的分析结构的要求也越来越高。
目前图像处理已经发展出很多分支，包括图像分割、边缘检测、纹理分析、图像压缩等.经验模式分解（EMD）是希尔伯特—黄变换（Hilbert—HuangTransform)中的一部分，它是一种新的信号处理方法，并且在非线性、非平稳信号处理中取得了重大进步,表现出了强大的优势与独特的分析特点.该方法主要是将复杂的非平稳信号分解成若干不同尺度的单分量平稳信号与一个趋势残余项,所以具有自适应性、平稳化、局部性等优点。
鉴于EMD方法在各领域的成功应用以及进一步的发展,国内外很多学者开始将其扩展到了二维信号分析领域中，并且也取得的一定的进展.但是由于二维信号不同于一种信号，限于信号的复杂性和二维数据的一些处理方法的有限性，二维经验模式分解(BEMD）在信号分析和处理精度上还存在一些问题,这也是本文要研究和改善的重点.关键词：图像处理;信号分解;BEMDAbstractIn recent years，with the rapid development of computer technology and the continuous improvement of signal processing technology，the demand for the analysis structure of the image is becoming more and more high. At present, many branches have been developed in image processing, including image segmentation, edge detection，texture analysis, image compression and so on。
Empirical mode decomposition (EMD) is a part of Hilbert Huang transform （Hilbert—HuangTransform）. It is a new signal processing method, and has made significant progress in nonlinear and non—stationary signal processing, showing strong advantages and unique analysis points。
