dlcwt

Load the ECG signal. The sampling frequency of the data is 180 hertz. Save the signal as a dlarray in "CBT" format.

load wecg
Fs = 180;
sig = dlarray(reshape(wecg,1,1,[]),"CBT");

Create a CWT filter bank that is compatible with the signal. Specify periodic boundary conditions.

fb = cwtfilterbank(SignalLength=length(sig),Boundary="periodic");

Use the wt object function to obtain the CWT coefficients of wecg. Also obtain the scaling coefficients. Concatenate the coefficients.

[cfsFB,~,~,scalcfs] = wt(fb,wecg);
allCFS = [cfsFB ; scalcfs];
whos allCFS

  Name         Size                Bytes  Class     Attributes

  allCFS      82x2048            2686976  double    complex   

Use the cwtfilters2array function to convert the filter bank to a reduced-weight tensor suitable for deep learning. Include the lowpass (scaling) filter in the tensor.

[psifvec,filteridx] = cwtfilters2array(fb,IncludeLowpass=true);

Obtain the deep learning CWT of the signal.

cfsD = dlcwt(sig,psifvec,filteridx);
dims(cfsD)

ans = 
'SCBT'

By default, the output is a dlarray object in "SCBT" format. The spatial dimension corresponds to frequency. Convert the output to a numeric array. Permute the dimensions of the output to correspond with "STCB" format. The result will be a 2-D matrix because there is only one channel and one batch.

cfs = extractdata(cfsD);
cfs = permute(cfs,[1 4 2 3]);
whos cfs

  Name       Size                Bytes  Class     Attributes

  cfs       82x2048            2686976  double    complex   

Confirm the CWT and deep learning CWT of the signal are equal.

max(abs(cfs(:)-allCFS(:)))

ans = 
1.0235e-09

Load the Espiga3 EEG dataset. The data consists of 23 channels of EEG sampled at 200 Hz. There are 995 samples in each channel. Save the multisignal as a dlarray, specifying the dimensions in order. dlarray permutes the array dimensions to the "CBT" shape expected by a deep learning network.

load Espiga3
Fs = 200;
[N,nch] = size(Espiga3);
x = dlarray(Espiga3,"TCB");
whos Espiga3 x

  Name           Size                Bytes  Class      Attributes

  Espiga3      995x23               183080  double               
  x             23x1x995            183110  dlarray              

Create a CWT filter bank that is compatible with the signal. Specify periodic boundary conditions. Then use the cwtfilters2array function to convert the filter bank to a reduced-weight tensor suitable for deep learning.

fb = cwtfilterbank(SignalLength=N,Boundary="periodic");
[psifvec,filteridx] = cwtfilters2array(fb);

Obtain the deep learning CWT of the multisignal.

cfsD = dlcwt(x,psifvec,filteridx);
dims(cfsD)

ans = 
'SCBT'

By default, the output is a dlarray object in "SCBT" format. The spatial dimension corresponds to frequency. Convert the output to a numeric array. Permute the dimensions of the output to correspond with "STCB" format. The result will be a 3-D array because there is only one batch.

cfs = extractdata(cfsD);
cfs = permute(cfs,[1 4 2 3]);
whos cfs

  Name       Size                   Bytes  Class     Attributes

  cfs       71x995x23            25997360  double    complex   

Obtain the center frequencies from the original filter bank. Display the scalogram of a channel.

frq = centerFrequencies(fb);
channel = 4;
cfsChannel = cfs(:,:,channel);
tms = (0:N-1)/Fs;
surface(tms,frq,abs(cfsChannel))
set(gca,"yscale","log")
axis tight
shading flat
title("Scalogram")
xlabel("Time (s)")
ylabel("Frequency (Hz)")