Synchronising two different signals

Question

Marpe 2022-10-15

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1827518-synchronising-two-different-signals

评论： Star Strider 2022-10-15

Data.zip

Hello,

I am quite new to signal processing and I am trying my best to synchronize and align two signals that come from different devices.

One has a sample rate of 50 Hz and is recording acceleration with 3 axis (x, y and z). The other is recording ECG at 250Hz.

I have time marks of when both devices are turned on and off (which is different by max 10-20 seconds). Both seems to have 'time-drift', meaning that when looking at the data size, it never perfectly equals the size of Time*SampleRate. And even after resampling the 250Hz signal at 50Hz both data sizes have a small difference.

I have been trying different methods to align those signals but there is always a little delay between the two of them and I don't know what to do. I tried removing the time drift, synchronizing them with time-series, even the cross-correlation (even though I am not sure this can work since one of my signal has 3 axis)… Nothing seems to work.

So is there a way to know if there is major problem in one of the signals ?

Or is there another way of aligning/synchronizing those signals ?

I linked the two unfiltered signals.

Thanks,

Marine

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Star Strider 2022-10-15

在 MATLAB Online 中打开

What are we to do with these?

Uz = unzip('https://www.mathworks.com/matlabcentral/answers/uploaded_files/1157478/Data.zip')
Uz = 1×8 cell array
    {'Data/'}    {'__MACOSX/._Data'}    {'Data/.DS_Store'}    {'__MACOSX/Data/._.DS_Store'}    {'Data/ECG.csv'}    {'__MACOSX/Data/._ECG.csv'}    {'Data/Acceleration.csv'}    {'__MACOSX/Data/._Acceleration.csv'}
EKG = readtable(Uz{5})
EKG = 904000×1 table
ECG_DATA________

       680  
      1372  
      1740  
      1774  
      1805  
      1832  
      1853  
      1870  
      1883  
      1889  
      1884  
      1870  
      1850  
      1832  
      1815  
      1800  
Acc = readtable(Uz{7})
Acc = 181412×18 table
Var1Var2Var3Var4Var5Var6Var7Var8Var9Var10Var11Var12Var13Var14Var15Var16Var17Var18____________________________________________________________________________________________________

    -106    -203    -1969    -157    151     -17      -32      54    -178    {0×0 char            }     NaN      NaN      NaN        NaN     NaN       NaN     NaN      NaN 
      18     -87    -2052    -169    159     -17     -230     309    -296    {0×0 char            }     NaN      NaN      NaN        NaN     NaN       NaN     NaN      NaN 
     129     -13    -2062    -169    151     -13     -140     245    -250    {0×0 char            }     NaN      NaN      NaN        NaN     NaN       NaN     NaN      NaN 
     108     -85    -2073    -159    149     -23      -93     166    -129    {0×0 char            }     NaN      NaN      NaN        NaN     NaN       NaN     NaN      NaN 
      66    -152    -2119    -169    149     -15       47      89    -143    {0×0 char            }     NaN      NaN      NaN        NaN     NaN       NaN     NaN      NaN 
      81    -114    -2010    -165    145     -11      116       4    -155    {0×0 char            }     NaN      NaN      NaN        NaN     NaN       NaN     NaN      NaN 
      59    -109    -2118    -170    148     -26       68      77    -144    {0×0 char            }     NaN      NaN      NaN        NaN     NaN       NaN     NaN      NaN 
     154     -53    -1999    -163    143     -25      189     -35    -117    {0×0 char            }     NaN      NaN      NaN        NaN     NaN       NaN     NaN      NaN 
     125      12    -1975    -158    156     -26      221    -132      56    {0×0 char            }     NaN      NaN      NaN        NaN     NaN       NaN     NaN      NaN 
       9    -110    -2064    -162    142     -20      220    -267     108    {'22-10-11T09:55:47Z'}     105      266      236     1016.5    0.06     25.17       0      4.1 
     -59    -226    -1893    -162    152     -26      220    -182      78    {0×0 char            }     NaN      NaN      NaN        NaN     NaN       NaN     NaN      NaN 
    -107    -329    -2100    -160    152     -22     -321     271    -124    {0×0 char            }     NaN      NaN      NaN        NaN     NaN       NaN     NaN      NaN 
      13    -156    -2148    -158    154     -28     -371     359    -235    {0×0 char            }     NaN      NaN      NaN        NaN     NaN       NaN     NaN      NaN 
     197      -5    -2076    -166    154     -26     -249     290    -205    {0×0 char            }     NaN      NaN      NaN        NaN     NaN       NaN     NaN      NaN 
     175     -65    -2140    -162    154     -22       21      32     -70    {0×0 char            }     NaN      NaN      NaN        NaN     NaN       NaN     NaN      NaN 
      84    -178    -1973    -159    141     -27      201    -198     -47    {0×0 char            }     NaN      NaN      NaN        NaN     NaN       NaN     NaN      NaN 

Just looking at them, I have no idea.

.

Marpe 2022-10-15

trial3.m

Yes sorry I forgot to share my code. This one of my trial to better understand.

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Answer 1

Star Strider 2022-10-15

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1827518-synchronising-two-different-signals#answer_1075918

在 MATLAB Online 中打开

I got this far, however I have no idea what you want to do with them at this point —

% clear

% close all

% datapathECG = %Data path;

% dECG = dir(datapathECG);

% nd = length(dir);

Uz = unzip('https://www.mathworks.com/matlabcentral/answers/uploaded_files/1157478/Data.zip');

% EKG = readtable(Uz{5})

% Acc = readtable(Uz{7})

%ECG data

for n=10

% file=[datapathECG,dECG(n).name];

% tbl = readtable(file);

tbl = readtable(Uz{5});

ecg=table2array(tbl);

[~,~]=size(ecg);

Fs1 = 250;

%Resampling of ECG from 250Hz to 50Hz

ecg=resample(ecg,50,Fs1);

[Necg,~]=size(ecg);

end

%Acceleration data

% close all

% datapathAcc= % Data path to data files

% dAcc=dir(datapathAcc);

% nd=length(dir);

for n=5

% file=[datapathAcc,dAcc(n).name];

% tbl = readtable(file);

tbl = readtable(Uz{7});

acctab=tbl(:,1:3);

Tacc=tbl(:,10);

acctab = table2array(acctab);

[Nacc,dum]=size(acctab);

Fs2=50;

end

%Remove drift in both

%ECG

StartECG = datetime(2022,10,11,07,56,40,'TimeZone','UTC');

StartECG.Format = 'hh:mm:ss';

Stop = datetime(2022,10,11,08,57,10,'TimeZone','UTC');

Stop.Format = 'hh:mm:ss';

Fs = 50;

Time = seconds(Stop - StartECG);

Nth = (Time.*Fs);

drift = Nth - Necg;

drift = drift/Fs;

drift = Nth/drift;

drift1 = round(drift/Fs);

ecg(drift1:drift1:end,:) = [];

[Necg,d]=size(ecg);

%Acceleration

DT = datetime(Tacc.Var10, 'InputFormat','yy-MM-dd''T''HHH:mm:ss''Z''','TimeZone','UTC');

DT.Format = 'hh:mm:ss';

DTnew = fillmissing(DT,'linear');

StartR = DTnew(1,1);

disp('Real = ');

Real =

disp(StartR);

09:55:47

StartR = datetime(StartR,"TimeZone","UTC");

StartR.Format = 'hh:mm:ss';

%Have to change everytime

StartTH = datetime(2022,10,11,07,55,20,'TimeZone','UTC');

StartTH.Format = 'hh:mm:ss';

disp('Theory = ');

Theory =

disp(StartTH);

07:55:20

delay = abs(StartR - StartTH);

StartACC = StartR - delay;

Stop = datetime(2022,10,11,08,56,10,'TimeZone','UTC');

Stop.Format = 'hh:mm:ss';

Fs = 50;

Time = seconds(Stop - StartACC);

Nth = (Time.*Fs);

drift = Nth - Nacc;

drift = drift/Fs;

drift = Nth/drift;

drift2 = round(drift/Fs);

acctab(drift2:drift2:end,:) = [];

[Nacc,d]=size(acctab);

%Time for ECG

time=StartECG;

time.Format= 'hh:mm:ss';

s1=seconds(1/Fs2:1/Fs2:Necg/Fs2);

T=time+s1;

Tecg = timetable(T',ecg);

%Time for Acceleration data

time=StartACC;

time.Format= 'hh:mm:ss';

s1=seconds(1/Fs2:1/Fs2:Nacc/Fs2);

T=time+s1;

Tacc = timetable(T',acctab);

TT = synchronize(Tecg,Tacc,"union","nearest");

stackedplot(TT.Time,TT.Variables);

Or, to put it differently, I do not see a problem. You have several different signals all sharing the same time vector. (I have no idea what they refer to, or what you want to wo with them.)

.

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Star Strider 2022-10-15

I have never done anything with ballistocardiography. An interesting study is Estimation of the respiratory rate from ballistocardiograms using the Hilbert transform and there are several other open-source articles in a Pub Med search i did just now, many of which appear to be interesting.

The data need to be pre-processed if you are to make any sense of them. First calculate Fourier transforms (fft or pspectrum) on all of them to determine the appropriate filter characteristics, then filter them. Geting rid of as much of the noise as possible is important.

There will be a delay between the ECG, pulse (most reliably detected and characterised by LED plethysmocardiography) and ballistocardiography signals because there is inherent inertia in all the physical responses. (There are delays between the carotid and pedal pulses simply because of the transit time of the pulse wave, the physical characteristics of the arterial system, and the distance between them, for example.) The ventricles require time after the ECG excitation signal to contract and then to eject the blood that filled them during diastole. They all have mass, viscosity, and elasticity. (I modeled all this decades ago simply out of curiosity and because I had easy access to all the signals. I am certain that much better models now exist that you could identify and estimate.) So when you have filtered the signals to get rid of as much of the noise as possible, it could be interesting to use the System Identification Toolbox functions to see if a model characterising them could be identified and estimated.

I leave that to you!

Marpe 2022-10-15

Thank you very much for your help !

Star Strider 2022-10-15

My pleasure!

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