Find peaks/valleys of a noisy signal

Question

Jay Vaidya 2020-12-18

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链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/696995-find-peaks-valleys-of-a-noisy-signal

评论： Image Analyst 2020-12-19

I have this signal which is noisy as well as it has too much data samples. When I try to find the peaks or valleys, it gives multiple peaks/valleys around the same point probably because the data is noisy and has too many samples. I did use the 'MinPeakDistance' and also tried using the 'MinPeakHeight' and also the 'Threshold' but all time I get many peaks's around a given time instant. In other words, I would want only one peak at the peak of the signal and one valley at the trough of the signal. I have the data attached to the post too. Thanks in advance.

It is just a two column data and I plot the 2nd column wrt 1st one. I would prefer to measure valleys and I would actually need both.

[pks locs] = findpeaks(data_compact(:,2),'MinPeakHeight',0.992*max(data_compact(:,2)),'MinPeakDistance',5000e-3); % peaks
data_inverted(:,1) = data_compact(:,1);
data_inverted(:,2) = -data_compact(:,2);
%[valley valleys_locs] = findpeaks(data_inverted(:,2),'MinPeakDistance',0.2e-3); % valleys

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Jay Vaidya 2020-12-18

编辑：Jay Vaidya 2020-12-18

Yes, but I would like to put another data instead of this data, as this is not that important to what I am intending to do. I also cannot delete the question. I currently don't need to solve this problem and the data can be misused.

Walter Roberson 2020-12-18

No-one knows what your data means, or who or what it was created from. It is therefore difficult to "misuse".

https://en.wikipedia.org/wiki/Streisand_effect

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Answer 1

Image Analyst 2020-12-18

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/696995-find-peaks-valleys-of-a-noisy-signal#answer_579315

编辑：Image Analyst 2020-12-19

在 MATLAB Online 中打开

data_compact.mat

Try this:

clc;    % Clear the command window.
clear all;
close all;
workspace;  % Make sure the workspace panel is showing.
format short g;
format compact;
fontSize = 22;
fprintf('Beginning to run %s.m ...\n', mfilename);
%--------------------------------------------------------------------------------------------------
% Load data from mat file.
s = load('data_compact.mat')
data_compact = s.data;
x = data_compact(:,1);
% Plot data.
plot(x, data_compact(:,2), 'b-');
xlim([x(1), x(end)]);
grid on;
hold on;
%--------------------------------------------------------------------------------------------------
% Smooth with a savitzky-golay filter.  Polynomial order = 2, window width = 351 elements.
smoothY = sgolayfilt(data_compact(:, 2), 2, 351);
plot(x, smoothY, 'r-');
%--------------------------------------------------------------------------------------------------
% Find peaks.  Must be separated by 13000 elements.
[peakValues, indexesOfPeaks, widths, proms] = findpeaks(smoothY, 'MinPeakDistance',13000); % peaks
% Remove an occasional outlier that is below the midpoint.
meanSignal = mean(smoothY);
outlierIndexes = peakValues < meanSignal;
peakValues(outlierIndexes) = [];
indexesOfPeaks(outlierIndexes) = [];
% Plot peaks.
plot(x(indexesOfPeaks), peakValues, 'g.', 'MarkerSize', 30);
%--------------------------------------------------------------------------------------------------
% Find Valleys.  Must be separated by 13000 elements.
[valleyValues, indexesOfValleys] = findpeaks(-smoothY, 'MinPeakDistance', 13000); % valleys
valleyValues = -valleyValues; % Make upright again.
% Remove an occasional outlier that is above the midpoint.
outlierIndexes = valleyValues > meanSignal;
valleyValues(outlierIndexes) = [];
indexesOfValleys(outlierIndexes) = [];
% Plot valleys.
plot(x(indexesOfValleys), valleyValues, 'c.', 'MarkerSize', 30);
message = sprintf('Found %d peaks, and %d valleys', length(indexesOfPeaks), length(indexesOfValleys));
title(message, 'FontSize', fontSize);
% Maximize the figure window.
g = gcf;
g.WindowState = 'maximized'
fprintf('%s\n', message);
uiwait(helpdlg(message));
fprintf('Done running %s.m ...\n', mfilename);