Pattern Adapted Wavelets for Signal Detection

The purpose of this example is to show how to use the continuous wavelet transform (CWT) to detect patterns in signals.

First, we consider a simple detection problem. A signal S(t) is defined. It contains a superposition of dilated and translated versions of a given basic pattern f(t). In this context, when only S(t) and f(t) are known, we have to identify the number of patterns and the values of the scale-position pairs. To achieve this goal, the following process is used:

1- Construct a pattern adapted wavelet approximating the form f(t) using one of the methods available in the toolbox.

2- Obtain the CWT of the signal S using the pattern adapted wavelet.

3- Estimate the scale and position of the pattern by locating the maxima of the absolute values of the CWT coefficients.

This technique will be initially used on simulated signals and then applied to spike detection in EEG data.

% Last Revision: 10-Jun-2013 (Yves and Michel Misiti)

Haar Pattern Detected by Haar Wavelet

The first example illustrates how the maxima of the absolute values of the CWT coefficients can be used to estimate the position and scale of a signal pattern. To construct the signal to analyze, create a vector of zeros of length 897. We insert in the vector of zeros the basic form of the Haar wavelet of length 90 centered at index 385.

% Length of the signal and half-length of the pattern.
lenSIG = 897;
L = 45;

% Length, beginning, end and middle of the pattern.
long  = 2*L;
first = 340;
last  = first+long-1;
pat = [-ones(1,L) ones(1,L)];

The problem is to detect this pattern. Here we use the Haar wavelet, which is well-suited for this detection problem because it is matched to the pattern.

% Signal with inserted shape.
Z = zeros(1,lenSIG);
Z (first:last) = pat;
scales = 1:256;
wname = 'haar';

% Continuous wavelet transform (CWT).
c = cwt(Z,scales,wname,'scalCNT');

% Detect the maximum of the absolute value of coefficients.
[~,imax] = max(abs(c(:)));
SIZ = size(c);
ROW = rem(imax,SIZ(1));
if ROW==0
    ROW = SIZ(1);
[~,COL] = max(abs(c(ROW,:)));
display(sprintf('Detected indices COL and ROW: %3.0f %3.0f',COL,ROW))
Detected indices COL and ROW: 385  90

Direct inspection of the output plot of the CWT shows that both the center position of the pattern (index 385) and the scale (length 90) are perfectly detected. This appears more clearly when zooming in on the maximum in the scalogram.

Instant and Duration Associated with a Detection.

In many contexts, it is natural to define the scales not in terms of the dilation factor, but rather in terms of the duration of the patterns to be detected. In order to establish the correspondence between scale and duration for a discrete-time sequence, it is necessary to know the sampling period.

To handle detection problems in terms of pattern duration, we define a second usage of the CWT taking into account the sampling period in the numerical approximation of the wavelet coefficients. Denoting the sampling period by stepSIG, the key point is to properly scale the discretized version of the wavelet in order to compute integrals on intervals [k,k+1]*stepSIG instead of [k,k+1].

Revisiting the preceding pattern detection problem, assume that the sampling period of the sequence is 25 milliseconds.

% Set the sampling period.
stepSIG = 0.025;

The scale of the pattern is 90 samples. Using the sampling period, the duration of the pattern is 90*stepSIG (2.25) seconds. The pattern is centered at 385*stepSIG (9.60) seconds. In this example, we define the scales in terms of the pattern duration. The corresponding scales are then directly obtained by dividing by the sampling period (this is performed inside the cwt function).

scales  = (1:0.25:10);
positions = (0:lenSIG-1)*stepSIG;

% Sampling rate of the analyzing wavelet.
stepWAV = 1/1024;
wname   = 'haar';
WAV = {wname,stepWAV};

% Caution, SIG is a cell array containing the signal Z and the sampling
% period stepSIG.
SIG = {Z,stepSIG};

% Recall that scales are expressed as multiple of the sampling period.
c = cwt(SIG,scales,WAV,'scalCNT');

% Detect the maximum of the absolute value of coefficients.
[~,imax] = max(abs(c(:)));
SIZ = size(c);
ROW = rem(imax,SIZ(1));
if ROW==0
    ROW = SIZ(1);
[~,COL] = max(abs(c(ROW,:)));
display(sprintf('Detected indices COL and ROW: %3.0f %3.0f',COL,ROW))
display(sprintf('Duration: %4.2f',scales(ROW)))
display(sprintf('Instant:  %4.2f',positions(COL)))
Detected indices COL and ROW: 385   6
Duration: 2.25
Instant:  9.60

By incorporating the sampling period, you can translate position and scale into the temporal notions of instant and duration. These are estimated from the CWT as:

(385-1) * 0.025 = 9.60s and 90 * 0.025 = 2.25s

Constructing an Adapted Wavelet

It is well known that detection of a deterministic signal in white noise is optimized by the use of a filter whose impulse response is matched to the signal. The Wavelet Toolbox™ software enables you to design admissible wavelets based on the pattern you wish to detect. Designing a valid wavelet based on your pattern allows you to exploit the optimality of matched filtering in the framework of the CWT.

In designing your pattern adapted wavelet, you can define the pattern either as a function of time (which is given without any a priori reference to the signal), or extract the pattern from a sampled signal. The first transformation, which is in fact performed implicitly, is to map the pattern on the unit interval [0,1] using an arbitrary regular grid.

Then, the MATLAB® function pat2cwav computes a pattern-adapted wavelet defined on a grid of 256 points on the interval [0,1].

The last step is to compute the CWT using this wavelet and extract the local maxima in absolute value. Each maximum corresponds to an instant (fixed as the middle of the suitably rescaled pattern) and a duration (the length of the suitably rescaled pattern).

Note that in the CWT, each wavelet is rescaled in such a way that the associated pattern is supported on the interval [0,1]. So a fair comparison could be performed to evaluate the detection performance of the adapted wavelet versus a predefined well known wavelet. In this example we focus on the pattern adapted detection.

In the next example we illustrate how to use the CWT to identify translation and dilation factors corresponding to the original detection problem when the pattern is given without any a priori reference to the signal.

Two Translated Versions of a sine Pattern Detected using an Adapted Wavelet

We construct a signal S containing two dilated versions of a basic form F given by:

% Define the form to detect.
C1 = 1;
C2 = 0.9;
X = linspace(-1,1,256);
F = C1*(sin(pi*X)+abs(sin(pi*X)))/2 ...
    - C2*(sin(-pi*X)+abs(sin(-pi*X)))/2;

% Construct the signal containing two similar forms.
Z = zeros(1,2048);

% Insert the first form.
L = 128;
long  = 2*L;
first = 513;
last = first+long-1;
middle = (first+last)/2;
Z(first:last) = F;

% Insert the second form.
long2  = L;
first2 = 1473;
last2 = first2+long2-1;
middle2 = (first2+last2)/2;
Z(first2:last2) = sqrt(2)*F(1:2:end);

Since the integral of the form to detect is not zero, this form is not a wavelet. To use the CWT, we need to construct an approximating wavelet.

The principle for designing a new wavelet for the CWT is to approximate a given pattern using least squares optimization under constraints leading to an admissible wavelet.

% Build and display the adapted wavelet.
[Y,X] = pat2cwav(F,'orthconst',0,2);
hold on
title('Form to detect (b) and adapted Wavelet (r)');

To use the new constructed wavelet, it must be added to the available wavelets of the toolbox.

% Save the adapted wavelet and add it to the toolbox
locdir = cd;
save adp_FRM1 X Y
wavemngr('add','AdapF1','adpf1',4,'','adp_FRM1.mat',[0 1]);

Assume the sampling interval of the signal Z is 1/32 seconds. Construct an appropriate time axis and plot the signal

time = linspace(0,64,length(Z));
grid on;
ax = gca;
ax.XTick = [0 10 16 20 24 30 40 46 50];

From the figure, we see that one pattern is centered around 20 seconds and the second pattern is centered around 48 seconds. The pattern centered around 20 seconds has a duration of 8 seconds while the second pattern has a duration of 4 seconds.

% We analyze the signal by computing the CWT coefficients of Z using the
% admissible wavelet we constructed to approximate the basic form F.

stepSIG = 1/32;
stepWAV = 1/256;
wname = 'adpf1';
scales  = (1:2*long)*stepSIG;
WAV = {wname,stepWAV};
SIG = {Z,stepSIG};

% Detect the maximum of the absolute value of coefficients.
lenSIG = length(Z);
positions = (0:lenSIG-1)*stepSIG;
fprintf('Instant 1:  %4.2f\n',positions(round(middle)))
fprintf('Instant 2:  %4.2f\n',positions(round(middle2)))
Instant 1:  20.00
Instant 2:  48.00

The contour plots of the CWT coefficients show that the pattern-adapted wavelet is effective in locating the center positions of the patterns along the x-axis and the scales along the y-axis.

Detection of Two Superimposed Versions of a sine Pattern.

Let us consider another example, which is more difficult since we partially superimpose two translated and dilated versions of the same basic form (see the signal at the top of the next figure).

We first build a signal containing two superimposed similar forms.

% Construct the signal containing two similar forms.
Z = zeros(1,768);

% Insert the first form.
L = 128;
long  = 2*L;
first = 253;
last = first+long-1;
Z(first:last) = F;

% Insert the second form.
L2 = 16;
long2  = 2*L2;
first2 = 353;
last2 = first2+long2-1;
Z(first2:last2) = Z(first2:last2)+2*sqrt(2)*F(1:8:end);

The same pattern-adapted wavelet is used to detect the superimposed forms. Define the wavelet, the signal, and the wavelet sampling period.

wname = 'adpf1';
stepSIG = 1/8;
stepWAV = 1/256;
scales = (1:2*long)*stepSIG;

We analyze the signal with the adapted wavelet. The two dilated forms are clearly detected. The adapted wavelet is very accurate in locating the two scales, 32 and 8, at position 48.

SIG = {Z,stepSIG};
WAV = {wname,stepWAV};

Real World Example: Epileptic Spikes in EEG Signals

This part of the example is based on the paper by Hector Mesa entitled "Adapted wavelets for pattern detection", Lecture Notes in Computer Sciences, vol. 3773, 2005, p. 933-944.

The main objective of this real world example is to illustrate the capability of the pattern adapted wavelet procedure to highlight epileptic spikes in EEG signals by distinguishing them from the background brain activity.

We are going to briefly present the data. We will consider two different EEG datasets. The first one, Espiga3.mat, contains recordings of 995 samples obtained from 23 channels. In the following plot of the data, a single spike is observable in many of the channels around sample 650. Note that the spike is not equally well resolved in all the channels.

For instance, the spike cannot be detected in channel 23 which is dominated by the ECG recording. Contrast this to channel 1 where the spike is easy to identify. Let us focus on the channel 1 data in the next plot. Since the sampling period is 1/200 seconds, the total duration of the recording is approximately 4.97 seconds.

In the next three sections, we apply pattern adapted wavelet detection to EEG data. Specifically, we perform the following:

1 - Construct an admissible wavelet to approximate the spike pattern extracted from a single channel EEG recording. The pattern adapted wavelet is constructed from the channel one recording and tested on the same channel.

2 - Using the pattern adapted wavelet obtained in the first section, spike detection is performed in the other channels of the same EEG dataset.

3 - The pattern adapted wavelet is utilized for spike detection in a different EEG dataset.

Constructing Adapted Wavelets from a Single EEG Channel.

In the following plot of the channel 1 data, the spike is located between the two vertical lines drawn on the plot. The spike occurs in the interval from 3 to 3.5 seconds.

Using the GUI tools for designing pattern adapted wavelets (instead of the command line tool pat2cwav.m), and using different available approximation methods, four different wavelets are generated and plotted in the next figure: the pattern in blue and the adapted wavelet in red (all the functions are rescaled on the interval [0,1]).

From the top left to the bottom right, one can find the wavelets (denoted by adp7 to adp10) obtained using different set of parameters (ORTH_CONT, ORTH_NONE, Pol6_CONT, and Pol6_DIFF respectively). In the following examples, we exclusively use the adp8 wavelet (top right) to process the EEG data.

Applying the Adapted Wavelet to the Channel 1 Data

In this section, we use the adapted wavelet for the detection of the original pattern to verify the procedure.

% Load the channel 1 of the EEG record.
load eeg_3_01;
Z = eeg_3_01;

stepSIG = 1/200;       % sampling period of the EEG.
stepWAV = 1/1024;
stepSCA = 0.1;
lenSIG = length(Z);    % length of the record.
first  = 645;          % beginning of the pattern (spike).
middle = 663;          % middle of the pattern.
long   = 36;           % length of the pattern.
last   = first+long-1;
F = Z(first:last);     % pattern.

Add the adapted wavelets to the toolbox

wavemngr('add','AdpWave','adp',4,'7 8 9 10','adpwavf',[0 1]);

% Detection of the spike using the adapted wavelet.
scales = (1:stepSCA:2*long)*stepSIG;
positions = (0:lenSIG-1)*stepSIG;
SIG = {Z,stepSIG};          % Signal and sampling period.
WAV = {'adp8',stepWAV};     % Wavelet and sampling period.

Note that the adapted wavelet can be built either using the function pat2cwav (as shown in a previous section), or using the GUI, which allows you to design and save the new adapted wavelet interactively. See "Adding Your Own Wavelets" in the User's Guide for more information.

% Find the CWT of the EEG signal using the pattern adapted wavelet.
fig = figure;
c = cwt(SIG,scales,WAV,'scalCNT');
ax = findobj(fig,'Type','axes');
tag = ax.Tag;
sig_Axes = ax(strcmpi(tag,'SIG_Axes'));

% Detect the maximum of the absolute value of coefficients.
[~,imax] = max(abs(c(:)));
SIZ = size(c);
ROW = rem(imax,SIZ(1));
if ROW==0
    ROW = SIZ(1);
[maxi,COL] = max(abs(c(ROW,:)));

% Zoom in the axes on the region of interest.
ax = findobj(fig,'Type','axes');
Xlim  = [2.8 3.6];

% Position and Duration.
TimePos  = positions(COL)
Duration = scales(ROW)

CFS_Axes = ax(strcmpi(tag,'CFS_Axes'));
LW = 2;              % Linewidth
CL = [0.2 0.7 0.2];  % LineColor
propLINE = {'Color',CL,'LineWidth',LW,'LineStyle','--','Parent',CFS_Axes};
line('XData',[positions(1) positions(end)],'YData',[ROW ROW],propLINE{:});
line('XData',[TimePos TimePos],'YData',[1 length(scales)],propLINE{:});
TimePos =


Duration =


The maximum absolute value of the CWT coefficients occurs at 3.31 seconds and corresponds to a duration (scale) of 0.1835 seconds. Both the position and duration corresponding to the maximum show good agreement with the spike as expected. This is confirmed in the contour plot.

The detected position and duration are illustrated below by the portion of the signal and the adapted wavelet superimposed with the pattern.

Spike Detection on Other Channels of the Same EEG Dataset.

Discarding the channels for which the signal to noise ratio is not sufficient, we have selected the 12 channels displayed on the left of the following figure:

From top to bottom in the right portion of the figure, you can find the following information.

- the 12 portions of the signals containing the spikes;

- the adapted wavelet superimposed with the pattern (spike of channel 1);

- the table of the 12 detected positions and durations.

As shown in the table, the estimated center position and duration show good agreement across all 12 channels.

Spike Detection on a Different EEG Dataset with the Same Adapted Wavelet.

In this section, we use the wavelet adapted to the pattern extracted from channel 1 of the previous EEG dataset and apply it to second EEG dataset consisting of 23 channels. The EEG data contain 1001 samples with a sampling period of 1/200 seconds, leading to a total duration of 5 seconds.

Repeating the procedure used above, the left part of the following figure displays the data from the 12 usable channels. The right part of the figure displays the spike patterns from the 12 channels, the pattern-adapted wavelet, and the table of estimated center positions and durations.

The estimated center position and duration again show good agreement across the 12 channels. This illustrates that the wavelet adapted from a spike pattern in the first EEG dataset is effective in detecting spike patterns in a different EEG dataset.

We now delete the newly-created wavelets and temporary files.

% Delete the adapted wavelets and the wavelet files.


This example has shown you how to use pattern adapted wavelets with the CWT for signal detection. First, we have shown how to use the local absolute maxima in the CWT to detect a simple Haar pattern with the Haar Wavelet. Next, two translated versions of a sine pattern are detected using an adapted wavelet. Subsequently, a more difficult problem is addressed: the detection of two superimposed versions of a sine pattern. Finally, pattern adapted wavelets are used in a real world example: the detection and localization of epileptic spikes in EEG signals.

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