dlicwt
Description
Examples
Invert Deep Learning Continuous Wavelet Transform of Multisignal
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,ch] = size(Espiga3); dlx = dlarray(Espiga3,"TCB"); whos Espiga3 dlx
Name Size Bytes Class Attributes Espiga3 995x23 183080 double dlx 23x1x995 183110 dlarray
dims(dlx)
ans = 'CBT'
Create a CWT filter bank that is compatible with the channels. Specify periodic boundary conditions. Then use the cwtfilters2array
function to convert the filter bank to a reduced-weight tensor suitable for deep learning. Include the lowpass filter in the tensor.
fb = cwtfilterbank(SignalLength=N,SamplingFrequency=Fs, ... Boundary="periodic"); [psifvec,filteridx] = cwtfilters2array(fb,IncludeLowpass=true);
Obtain the deep learning CWT of the multisignal.
dlcfs = dlcwt(dlx,psifvec,filteridx);
whos dlcfs
Name Size Bytes Class Attributes dlcfs 72x23x1x995 26363528 dlarray complex
dims(dlcfs)
ans = 'SCBT'
To reconstruct the signal, compute the deep learning inverse CWT.
dlxrec = dlicwt(dlcfs,psifvec,filteridx);
whos dlxrec
Name Size Bytes Class Attributes dlxrec 23x1x995 183086 dlarray
dims(dlxrec)
ans = 'CBT'
Convert the output to a numeric array. To match the dimensions of the original multisignal, permute the output dimensions to correspond with the "TCB"
format. The result is a matrix because there is only one batch.
xrec = extractdata(dlxrec); xrec = permute(xrec,[3 1 2]); whos xrec Espiga3
Name Size Bytes Class Attributes Espiga3 995x23 183080 double xrec 995x23 183080 double
Choose two of the channels. Compare the original channel with its reconstruction.
channelIdx = [2 6]; tiledlayout(2,1) for k=1:2 idx = channelIdx(k); nexttile xorig = Espiga3(:,idx); xreco = xrec(:,idx); plot(xorig) ylabel("Channel "+num2str(idx)) hold on plot(xreco,"--") hold off legend("Original","Reconstruction") title("MSE: "+mean((xorig-xreco).^2)) grid on end
Input Arguments
cfs
— Continuous wavelet transform
dlarray
object | numeric array
Continuous wavelet transform, specified as a formatted or unformatted dlarray
(Deep Learning Toolbox) or
numeric array. cfs
is the CWT of a real-valued signal.
If
cfs
if a formatteddlarray
, it must be in the"SCBT"
or"CBT"
format.If
cfs
is in the"CBT"
format, thedlicwt
function unflattenscfs
to the"SCBT"
shape.If
cfs
is real-valued, the number of channelsC
must be even because thedlicwt
function assumes that the true number of channels isC
/2.If
cfs
is an unformatteddlarray
, it must be compatible with the"SCBT"
or"CBT"
formats and you must setDataFormat
.
The dlicwt
function computes the inverse CWT along the
"S"
dimension of cfs
. The
"S"
dimension corresponds to scale, or equivalently, the center
frequency of the wavelet bandpass filters.
Data Types: single
| double
Complex Number Support: Yes
psifvec
— Reduced-weight CWT filter bank
dlarray
object | numeric array
Reduced-weight CWT filter bank, specified as a real-valued
1-by-1-by-Nr tensor, where Nr is the number of
weights in the filter bank. Use cwtfilters2array
to obtain psifvec
.
Data Types: single
| double
filteridx
— Bookkeeping matrix
matrix
Bookkeeping matrix, specified as a matrix. In order to invert the CWT, the
dlicwt
function uses filteridx
and
psifvec
to reconstruct an approximation to the CWT filter bank.
Use cwtfilters2array
to obtain filteridx
.
You can use array2cwtfilters
to reconstruct the CWT filter bank from
psifvec
and filteridx
.
fmt
— CWT format
character vector | string scalar
CWT format, specified as a character vector or string scalar. This argument is valid
only if cfs
is unformatted.
Each character in this argument must be one of these labels:
"S"
—Spatial (scale) dimension"C"
— Channel"B"
— Batch observations"T"
— Time
The dlicwt
function accepts any permutation of
"CBT"
or "SCBT"
. Each element of the argument
labels the matching dimension of cfs
. You can specify at most one
of each of the "S"
, "C"
, "B"
,
and "T"
labels.
Example: x = dlicwt(cfs,psifvec,filteridx,DataFormat="SBCT")
specifies the format of the unformatted dlarray
object
cfs
as "SBCT"
.
Output Arguments
x
— Reconstructed signal
dlarray
object
Reconstructed signal, returned as a formatted or unformatted
dlarray
object.
If
cfs
is a formatteddlarray
, thenx
is a"CBT"
formatteddlarray
.If
cfs
is an unformatteddlarray
, thenx
is an unformatteddlarray
. The dimension order inx
is"CBT"
.
Data Types: single
| double
Version History
Introduced in R2024b
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