MATLAB: Extract valuable data from signal

split signal

I have a signal (vector) consists of many blocks (for example five blocks）.I want to extract, separate, these blocks (that contain a valuable information ) from the main signal and store every block in a vector.
For example like: https://encrypted-tbn3.gstatic.com/images?q=tbn:ANd9GcRVDJyqxKO-48fA07OdnPPLfPIwFpXg8AqbmoDwAQ-X4dK-5bOJ
So if I have an input vector (V), The result , in our case, should be five mini vectors (v1, v2, v3, v4, v5).
I tried to apply this method: If a specific consecutive elements from (V) vector have a value above a threshold (for example 0.02 > Thr) start put the elements in a a mini vector, but it does not work because values in the input vector (V) are getting positive and negative.

Best Answer

Try something like this, where I take the absolute value, then filter it to get rid of the oscillating parts, then take the difference, and finally extract the 5 bursts:
clc;    % Clear the command window.
close all;  % Close all figures (except those of imtool.)
clear;  % Erase all existing variables. Or clearvars if you want.
workspace;  % Make sure the workspace panel is showing.
format longg;
format compact;
fontSize = 20;
% Generate sample data.
signal = 0.003 * rand(1, 550) - 0.0015
signal(50:100) = 0.03 * rand(1, 51) - 0.015
signal(150:200) = 0.03 * rand(1, 51) - 0.015
signal(250:300) = 0.03 * rand(1, 51) - 0.015
signal(350:400) = 0.03 * rand(1, 51) - 0.015
signal(450:500) = 0.03 * rand(1, 51) - 0.015
subplot(3, 1, 1);
plot(signal);
title('Original Signal', 'FontSize', fontSize);
grid on;
% Enlarge figure to full screen.

set(gcf, 'units','normalized','outerposition',[0 0 1 1]);
% Take absolute value and median filter to get rid of oscillations.
filteredSignal = medfilt1(abs(signal), 13);
subplot(3, 1, 2);
plot(filteredSignal);
title('Filtered Signal', 'FontSize', fontSize);
% Find the quiet parts between the bursts.
quietParts = filteredSignal < 0.002;
subplot(3, 1, 3);
plot(quietParts, 'LineWidth', 3);
ylim([0 1.2]);
title('Quiet Parts', 'FontSize', fontSize);
% Find the starting and ending elements of the bursts.
startingBlockIndexes = find(diff(quietParts) < 0)
endingBlockIndexes = find(diff(quietParts) > 0)
% Extract the 5 blocks (known to be exactly 5)
v1 = signal(startingBlockIndexes(1):endingBlockIndexes(1));
v2 = signal(startingBlockIndexes(2):endingBlockIndexes(2));
v3 = signal(startingBlockIndexes(3):endingBlockIndexes(3));
v4 = signal(startingBlockIndexes(4):endingBlockIndexes(4));
v5 = signal(startingBlockIndexes(5):endingBlockIndexes(5));
% Plot the 5 signals.
figure;
subplot(5, 1, 1);
plot(v1);
grid on;
title('V1', 'FontSize', fontSize);
subplot(5, 1, 2);
plot(v2);
grid on;
title('V2', 'FontSize', fontSize);
subplot(5, 1, 3);
plot(v3);
grid on;
title('V3', 'FontSize', fontSize);
subplot(5, 1, 4);
plot(v4);
grid on;
title('V4', 'FontSize', fontSize);
subplot(5, 1, 5);
plot(v5);
grid on;
title('V5', 'FontSize', fontSize);
% Enlarge figure to full screen.
set(gcf, 'units','normalized','outerposition',[0 0 1 1]);
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