MATLAB Answers

Find peaks/valleys of a noisy signal

168 views (last 30 days)
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
  7 Comments
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".

Sign in to comment.

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 = 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);
  6 Comments

Sign in to comment.

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.
  1 Comment
Jay Vaidya
Jay Vaidya on 18 Dec 2020
No, I have not. Let me see that.

Sign in to comment.

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!