Automatic Signal segmentation for feature extraction
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Hi,
Does anyone know how to do signal segmentation on the raw signal? I need to segment the raw signal into 8 different segments so that i can do feature extraction on individual segments.
2 个评论
Star Strider
2018-1-21
What is the corresponding time vector that corresponds to those samples, or the sampling frequency?
Radons
2018-1-21
采纳的回答
Image Analyst
2018-1-21
Attach some signals so we can play with them.
Assuming the "tall" parts of the signal are not uniformly spaced (in which case you could extract them trivially with indexing at fixed indexes), then you need to find the center of the quiet parts. I'd start by thresholding the absolute value of the signal to find the low/quiet parts. You should have 9 of them, unless your tall signal parts hit the edge of your range. If you don't have 9, then I'd call imdilate repeatedly until you get exactly 9 quiet regions. Then I'd call regionprops() to get the centroid (middle index) of the quiet parts, and then I'd loop over the regions extracting the tall regions into cells. They need to be in cells because each region might have a different number of elements. Something like (untested)
quietParts = abs(signal) < threshold;
[~, numRegions] = bwlabel(quietParts);
while numRegions > 9
quietParts = imdilate(quietParts, [1,1,1]);
[~, numRegions] = bwlabel(quietParts);
end
% Now we should have the quiet parts. Find the centroids.
props = regionprops(quietParts, 'Centroid');
% Extract each tall signal part into a cell
for k = 1 : length(props)-1
index1 = round(props(k).Centroid;
index2 = round(props(k+1).Centroid;
individualSignals{k} = signal(index1:index2);
end
Note, the code above requires the Image Processing Toolbox.
19 个评论
Radons
2018-1-21
Image Analyst
2018-1-21
Like the parts with amplitude less than about 0.01 or so.
Image Analyst
2018-1-21
This works:
s = load('signal.mat')
signal = s.x;
subplot(2, 1, 1);
plot(signal, 'b-');
grid on;
threshold = 0.004;
subplot(2, 1, 2);
filteredSignal = sgolayfilt(abs(signal), 2, 2001);
plot(filteredSignal, 'b-');
grid on;
hold on;
quietParts = abs(filteredSignal) < threshold;
[~, numRegions] = bwlabel(quietParts)
while numRegions > 9
quietParts = imdilate(quietParts, [1,1,1]);
[~, numRegions] = bwlabel(quietParts);
end
% Now we should have the quiet parts. Find the centroids.
props = regionprops(quietParts, 'Centroid');
% Extract each tall signal part into a cell
for k = 1 : length(props)-1
index1 = round(props(k).Centroid(1));
index2 = round(props(k+1).Centroid(1));
line([index1, index1], ylim, 'Color', 'r');
line([index2, index2], ylim, 'Color', 'r');
individualSignals{k} = signal(index1:index2);
end
% Make a new figure.
figure
for k = 1 : length(individualSignals)
subplot(3, 3, k);
plot(individualSignals{k}, 'b-');
grid on;
end
Radons
2018-1-22
Image Analyst
2018-1-22
For the 0.04, I just looked at the plots to find a threshold where about 8 peaks would be detected.
For the frame length, I just experimented until the smoothed curves showed the peaks and valleys well without too much noise. The shorter the frame length, the more closely it will look like the original noisy signal, making it harder to find the 8 peaks and 9 valleys.
Image Analyst
2018-1-25
What are the peaks? For each chunk there are dozens of peaks.
Image Analyst
2018-1-25
Just threshold each chunk at 0.01 or whatever is just above the noise level, like this:
thisSignal = individualSignals{k}
index1 = find(thisSignal > 0.01, 1, 'first');
index2 = find(thisSignal > 0.01, 1, 'last');
croppedSignal = thisSignal(index1:index2);
Radons
2018-1-25
Image Analyst
2018-1-25
You can try sgolayfilt with a shorter window to smooth it, then perhaps use findchangepts() to identify where it starts climbing.
Image Analyst
2022-11-1
It means you altered my code so that you're not assigning "individualSignals". Why did you change it to not assign those? Or else you're using a signal that should use a different threshold than I used for the original poster.
If you have any more questions, then attach your data and code to read it in with the paperclip icon after you read this:
@Cey your mat file does not have a signals variable in it
s = load('matlab.mat')
data = s.signal;
So what do you think you should do? How about getting the data field of s rather than the signal field?
Image Analyst
2022-11-2
I guess you didn't get the hint. Your field is called data so you should do
s = load('matlab.mat')
data = s.data;
Walter Roberson
2022-11-2
That code does not create individualSignals at all in the case that either no regions or exactly one region are found in the data. You should initialize it with
individualSignals = cell(length(props)-1, 1);
Image Analyst
2022-11-2
Your threshold of 0.004 is not good when used with your filtered signal. So you get no regions. And you're filtering it WAY too much, so much so that you've pretty much lost any resemblance of the original signal.
Cey
2022-11-4
Thanks a lot, so what values should I get? @Image Analyst
Cey
2022-11-4
thank you for your help @Walter Roberson
Image Analyst
2022-11-4
更多回答（1 个）
Hristo Zhivomirov
2019-10-13
0 个投票
Hi, Radons!
I think that this the most suitable, easy-to-use and straightforward way.
Also, you can use the example.m file as reference.
All best,
Hristo
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