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Find peaks/valleys of a noisy signal

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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
Jay Vaidya
Jay Vaidya on 18 Dec 2020
Edited: Jay Vaidya on 18 Dec 2020
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
Walter Roberson on 18 Dec 2020
No-one knows what your data means, or who or what it was created from. It is therefore difficult to "misuse".

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Accepted Answer

Image Analyst
Image Analyst on 18 Dec 2020
Edited: Image Analyst on 19 Dec 2020
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 =;
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);
fprintf('Done running %s.m ...\n', mfilename);
Jay Vaidya
Jay Vaidya on 19 Dec 2020
Ok, thanks. Here it is. Please replace this file in both the posts with your older attachments.

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More Answers (1)

Image Analyst
Image Analyst on 18 Dec 2020
If there is noise on your peaks, have you tried sgolayfilt() with order 2 or 3? It's in the Signal Processing Toolbox.




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