Signal Labeler
Label signal attributes, regions, and points of interest, and extract features
Description
The Signal Labeler app is an interactive tool that enables you to label signals for analysis or for use in machine learning and deep learning applications. Using Signal Labeler, you can:
Label signal attributes, regions, and points of interest
Use logical, categorical, numerical, or string-valued labels
Automatically label signal peaks or bounded signal regions
Apply custom labeling functions
Import, label, and play audio signals
Use frequency and time-frequency views to aid labeling
Generate attribute and region-of-interest (ROI) labels from signal features
Add, edit, and delete labels or sublabels
Display selected subsets of signals and labels
Signal Labeler saves data as labeledSignalSet
objects. You can use labeledSignalSet
objects to train a network, classifier,
or analyze data and report statistics. You can also export labeledSignalSet
objects to Diagnostic Feature
Designer (Predictive Maintenance Toolbox).
For more information, see Use Signal Labeler App.
More
You need an Audio Toolbox™ license to label audio signals.
You need a Predictive Maintenance Toolbox™ license to export labeled signal sets to Diagnostic Feature Designer.
Open the Signal Labeler App
MATLAB® Toolstrip: On the Apps tab, under Signal Processing and Communications, click the app icon.
MATLAB command prompt: Enter
signalLabeler
.
Examples
Label ECG Signals and Track Progress
This example shows how to track your labeling progress and assess the quality of labels with the Dashboard. In this mode, you can quickly determine how many members are labeled and inspect the distributions of label values and durations in your data set. This step facilitates the process of obtaining complete and accurate data sets for machine learning.
Download and Prepare the Data
Use the QTdownload
function to download the electrocardiogram (ECG) signals from the publicly available QT database [1] [2] to a new temporary directory folder
. The code for this function is at the end of the example.
folder = QTdownload;
Each file contains an ECG signal ecgSignal
, a table of region labels signalRegionLabels
, and the sample rate variable Fs
. All signals have a sample rate of 250 Hz. The region labels correspond to three heartbeat morphologies:
P wave
QRS complex
T wave
Create a signal datastore that points to folder
. Specify the signal variable name ecgSignal
and the sample rate variable Fs
.
sds = signalDatastore(folder,SignalVariableNames="ecgSignal", ... SampleRateVariableName="Fs");
Create a subset of the datastore containing the first twenty files. Use this subset as the source for a labeledSignalSet
object.
subsds = subset(sds,1:20); lss = labeledSignalSet(subsds);
Label Regions of Interest
Open the Signal Labeler app and import the labeled signal set from the workspace. Plot the first signal in the data set. From the Display tab, select the panner and zoom to a smaller region of the signal for better visualization.
From the Labeler tab, define a categorical region-of-interest (ROI) label with P, QRS and T categories. Name the label BeatMorphologies
.
Create a custom labeling function labelECGregions
to locate and label the three different regions of interest. Code for the custom function appears later in the example. You can save the function in your current folder, on the MATLAB path, or add it in the app by selecting Add Custom Function
in the Automate Value gallery. See Custom Labeling Functions for more information.
Select BeatMorphologies
in the Label Definitions browser and choose the labelECGregions
function from the Automate Value gallery. Select Auto-Label
and then Auto-Label and Inspect Plotted
. Click Run. From the Display tab, zoom in on a region of the labeled signal and use the panner to navigate through time. If the labeling is satisfactory, click Save Labels to accept the labels and close the Autolabel tab. You can see the labels and their location values in the Labeled Signal Set Members browser.
Visualize Labeling Progress and Statistics
Select the Dashboard in the toolstrip of the Labeler tab. The progress bar shows 5% of members are labeled with at least one ROI label. This corresponds to 1/20 members in the data set. The label distribution pie chart shows the number of instances of each category for the selected label definition.
Close the dashboard and continue your labeling. Select Auto-Label
and then Auto-Label All Signals
to label the next four signals in the list. Check the box next to the signal names you want to label and then click OK.
Select the Dashboard again. The progress bar now shows 25% of members are labeled. Verify the distribution of each category (P, QRS, or T) is as expected. The Label Distribution
pie chart shows that each category makes up about a third of all label instances. Select the Time Distribution
histogram chart from the Plots gallery to view the average duration of the P and T waves and QRS complexes, including outliers. Notice the T waves have longer durations than the P waves and QRS complexes.
Display the Member Count
chart to better visualize the distribution of labels across members and number of instances. Most members in the data set have between 0–500 instances of P, QRS, and T regions.
Click on the progress bar plot and adjust the Threshold
in the toolstrip to count only members with at least 5000
labels. Now only three of the five labeled members are included in the count. Adjust the count threshold to better differentiate between labeled and unlabeled members based on your labeling requirements.
labelECGregions
Function
The labelECGregions
function uses a pretrained deep learning network to identify P, QRS and T heartbeat morphologies in ECG signals.
function [labelVals,labelLocs] = labelECGregions(x,t,parentLabelVal,parentLabelLoc,varargin) labelVals = cell(2,1); labelLocs = cell(2,1); if nargin < 5 Fs = 250; else Fs = varargin{1}; end % Download the pretrained network netfil = matlab.internal.examples.downloadSupportFile("SPT", ... "data/QTDatabaseECGSegmentationNetworks.zip"); %#ok<*UNRCH> unzip(netfil,fullfile(tempdir,"ECGnet")) load(fullfile(tempdir,"ECGnet","trainedNetworks.mat")) for kj = 1:size(x,2) sig = x(:,kj)'; predTest = classify(rawNet,sig,MiniBatchSize=50); msk = signalMask(predTest); msk.SpecifySelectedCategories = true; msk.SelectedCategories = find(msk.Categories ~= "n/a"); labels = roimask(msk); labelVals{kj} = labels.Value; labelLocs{kj} = labels.ROILimits/Fs; end labelVals = vertcat(labelVals{:}); labelLocs = cell2mat(labelLocs); end
QTdownload
Function
You can download the data files from https://www.mathworks.com/supportfiles/SPT/data/QTDatabaseECGData.zip or use the unzip
function to create a folder in your temporary directory with 210 MAT-files in it.
function folder = QTdownload localfile = matlab.internal.examples.downloadSupportFile("SPT", ... "data/QTDatabaseECGData1.zip"); unzip(localfile,tempdir) folder = fullfile(tempdir,"QTDataset"); end
References
[1] Goldberger, Ary L., Luis A. N. Amaral, Leon Glass, Jeffery M. Hausdorff, Plamen Ch. Ivanov, Roger G. Mark, Joseph E. Mietus, George B. Moody, Chung-Kang Peng, and H. Eugene Stanley. "PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals." Circulation. Vol. 101, No. 23, 2000, pp. e215–e220. [Circulation Electronic Pages; http://circ.ahajournals.org/content/101/23/e215.full].
[2] Laguna, Pablo, Roger G. Mark, Ary L. Goldberger, and George B. Moody. "A Database for Evaluation of Algorithms for Measurement of QT and Other Waveform Intervals in the ECG." Computers in Cardiology. Vol.24, 1997, pp. 673–676.
Related Examples
Programmatic Use
signalLabeler
signalLabeler
opens the Signal Labeler app.
Version History
Introduced in R2019aR2024b: Export labeled signal sets to Diagnostic Feature Designer
You can export signal sets labeled using Signal Labeler directly to Diagnostic Feature Designer (Predictive Maintenance Toolbox). You must have a Predictive Maintenance Toolbox license to use Diagnostic Feature Designer.
R2024a: Compute more types of spectra and spectrograms
Enjoy more flexibility when computing spectra or spectrograms in Signal Labeler. Control the window length and shape, the number of discrete Fourier transform points, and the resolution bandwidth.
R2024a: Member-by-member navigation moved to main app
Member-by-member navigation is in the main app. There is no need to enter a specialized mode.
Other changes in behavior include:
Show or hide a signal in the display by clicking its name in the time-domain view legend. Move the legend by clicking and dragging.
Resize the label viewer and the time-domain, frequency-domain, and time-frequency views of the signals you are labeling.
Show Markers and Normalize Y Axis options not supported.
To zoom in and out or switch to draw mode, use the axes toolbar. The only zooming action that supports context menu and keyboard shortcuts is Zoom to label.
To select labels in the label viewer, you must be in select mode.
When labeling regions of interest or points in draw mode, use the mouse to adjust location or size or type a location value. Fine-tuning location value using keyboard shortcuts is not supported.
R2023b: Classify sounds and label bounded signal regions
Classify and label sounds in audio signals using the YAMNet pretrained network. You must have Audio Toolbox and Deep Learning Toolbox™ to use this functionality.
Locate and label signal regions with values greater or smaller than a given threshold or inside or outside a given set of ranges.
See Also
Apps
Functions
Topics
- Use Signal Labeler App
- Import Data into Signal Labeler
- Import and Play Audio File Data in Signal Labeler
- Create or Import Signal Label Definitions
- Label Signals Interactively or Automatically
- Custom Labeling Functions
- Customize Labeling View
- Spectrogram Computation in Signal Labeler
- Feature Extraction Using Signal Labeler
- Dashboard
- Export Labeled Signal Sets and Signal Label Definitions
- Signal Labeler Usage Tips
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