文件列表:

EEMDdenosisingHilbert.m (3576, 2019-04-17)
axcor.m (1388, 2019-04-10)
ecg.mat (20024, 2019-04-10)
ecg1.mat (40024, 2019-04-10)
eemd.m (5154, 2019-04-10)
emax.m (853, 2019-04-10)
emin.m (867, 2019-04-10)
extrema.m (4209, 2019-04-10)
m.mat (1142130, 2019-04-10)
peakseek.m (1150, 2019-04-10)
新建文本文档.txt (6423, 2019-04-13)

# Cardiac-frecuency-and-ECG-signal-denoising-by-EEMD ECG signal denoising using Ensemble Empirical Mode Decomposition and R peak detection (cardiac frequency) using Hilbert Transform. The aim of this project is to filter and denoise a physiological signal (in this case I opted for cardiac signals ECG), by using a new approach of Ensemble Empirical Mode Decomposition (a novel approach for denoising biological signals). Moreover, to register the cardiac frequency by using Hilbert Transform. ## FILE STRUCTURE: *Axcor----To obtain the correlation index of signals. Optional to use, to compare between signals after applying a filter. *ecg.mat---Real ecg example #1. *ecg1.mat---Real ecg example #2. *eemd.m--- EEMD function. See the description below. *EEMDdenosisingHilbert.m---Main source code. The file to run. It has the implementation of the functions with the ecg signals. *emax--Returns the max points and their coordinates *emin--Returns the min points and their coordinates *extrema--this function ususally used for finding spline envelope (not used). *peakseek---It finds the peaks in a certain scope (not used, because Hilbert Transform is more accurate). ``` Example: ecg=load ('ecg1.mat'); % loading the signal ecg=struct2cell(ecg); ecg=cell2mat(ecg); ecg = (ecg - 1024)/200; % you have to remove "base" and "gain" ``` ### EEMD code was obtain from: https://github.com/benpolletta/HHT-Tutorial/blob/master/HuangEMD/eemd.m#L12 HHT-Tutorial/HuangEMD/eemd.m (GITHUB) References: Wu, Z., and N. E Huang (2008), Ensemble Empirical Mode Decomposition: a noise-assisted data analysis method. Advances in Adaptive Data Analysis. Vol.1, No.1. 1-41. code writer: Zhaohua Wu. footnote:S.C.Su 2009/03/04 ## EEMD function The EEMD descompose the signals into Intrinsic Mode Functions, which a few are taken to reconstruct the signal and remove noise and artifacts. To apply the EEMD function: ``` Example: imf=eemd(ecg1,.2,70); %Apply the EEMD to the noisy signal Arguments: ecg1:signal to be filtered .2->ratio of the standard deviation, 70->ensemble number ``` These values where set according to literature. ## Filtering and denoising the ECG signal To filter the signal I apply a 4th order digital butterworth bandpass and the eemd. ``` %% ECG signal denoising imf=eemd(ecg1,.2,70); %Apply the EEMD to the noisy signal .2->ratio of the standard deviation 70->ensemble number imfs=imf'; %transpose the imf's matrix reconstruction=imfs(4,:)+imfs(5,:)+imfs(6,:); %We consider that these 3 imf's possess the important information %4 order Butterworth filter bandpass .05-230Hz. fclowpass=230; % Low pass cut-off frequency 230Hz fchighpass=.05; % Low pass cut-off frequency .05Hz filterorder=4; %filter order [b,a]=butter(filterorder,[filterorder*fchighpass/Fs,2*fclowpass/Fs]); filtered_ECG=filter(b,a,reconstruction); ``` The Hilbert Transform is used to Emphasize the R peaks of the ECG and to have one unique r peak, as it was previously removed the noise and the artifacts. ## Image The image depicts the initial raw physiological signal and the final filtered signal with the respective R peaks (cardiac Frequency), including the Hilbert Transform.  The following image shows the filtered/denoised ECG signal (Without the Hilbert Transform). 

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