signalTimeFeatureExtractor
Description
Use signalTimeFeatureExtractor
to extract time-domain
features from a signal. You can use the extracted features to train a machine learning model
or a deep learning network.
Creation
Description
creates a
sFE
= signalTimeFeatureExtractorsignalTimeFeatureExtractor
object with default property values.
specifies nondefault property values of the sFE
= signalTimeFeatureExtractor(Name=Value
)signalTimeFeatureExtractor
object. For example, signalTimeFeatureExtractor(FeatureFormat="table")
sets the output format of the generated features to a table.
Properties
Main Properties
FrameSize
— Number of samples per frame
positive integer
Number of samples per frame, specified as a positive integer. The
object divides the signal into frames of the specified length
and extracts features for each frame. If you do not specify
FrameSize
, or if you specify
FrameSize
as empty, the object
extracts features for the whole signal.
Data Types: single
| double
FrameRate
— Number of samples between start of frames
positive integer
Number of samples between the start of frames, specified as a positive integer. The frame rate
determines the distance in samples between the starting points
of frames. If you specify FrameRate
, then
you must also specify FrameSize
. If you do
not specify FrameRate
or
FrameOverlapLength
, then the
object assumes FrameRate
to be equal to
FrameSize
. You cannot specify
FrameRate
and
FrameOverlapLength
simultaneously.
Data Types: single
| double
FrameOverlapLength
— Number of overlapping samples between consecutive frames
positive integer
Number of overlapping samples between consecutive frames, specified as a positive
integer. FrameOverlapLength
must be less than or equal to the frame
size. If you specify FrameOverlapLength
, then you must also specify
FrameSize
. You cannot specify
FrameOverlapLength
and FrameRate
simultaneously.
Data Types: single
| double
SampleRate
— Sample rate
[]
(default) | positive scalar
Input sample rate, specified as a positive scalar in hertz.
If you do not specify SampleRate
, the extract
function of the object assumes the signal
sampling rate as 2π Hz.
Data Types: single
| double
FeatureFormat
— Format of generated signal features
"matrix"
(default) | "table"
Format of the signal features generated by the extract
function, specified as one of these:
"matrix"
— Columns correspond to feature values."table"
— Each table variable corresponds to a feature value.
Note
You can generate features for multiple signals at once by specifying a datastore
object input in the extract
function. In this case, extract
returns
a cell array where each member corresponds to a feature matrix or table from a
signal member of the datastore. The format of the generated features in each member
follows the format specified in FeatureFormat
.
Data Types: char
| string
IncompleteFrameRule
— Rule to handle incomplete frames
"drop"
(default) | "zeropad"
Rule to handle incomplete frames, specified as one of these:
"drop"
— Drop the incomplete frame and do not use it to compute features."zeropad"
— Zero-pad the incomplete frame and use it to compute features.
This rule applies when the current frame size is less than the specified
FrameSize
property.
Data Types: char
| string
ScalarizationMethod
— Methods to convert feature vectors to scalar values
timeScalarFeatureOptions
object
Since R2024b
Methods to convert feature vectors to scalar values, specified as a timeScalarFeatureOptions
object.
You can specify methods to extract scalar values from the Features to Extract. Specify
scalarization methods for the feature extractor object by using the
ScalarizationMethod
name-value argument or the setScalarizationMethods
function.
If you specify
ScalarizationMethod
, thesignalTimeFeatureExtractor
object returns the corresponding scalar values for each feature vector using the scalarization method.To convert a feature vector to scalar feature values:
You must enable the feature for extraction by setting the feature name in the
signalTimeFeatureExtractor
object totrue
.You must specify the desired scalarization methods for each feature name using a cell array of character vectors or a string array and store the information in a
timeScalarFeatureOptions
object.
After that, the
extract
function:Extracts the vectors corresponding to each enabled feature.
Takes the list of scalarization methods compiled by the object and for each method computes the corresponding scalar value.
Concatenates the vector features and the scalar features.
If you do not specify
ScalarizationMethod
, thesignalTimeFeatureExtractor
object does not perform any scalarization.
For more information about scalarization methods, see Scalarization Methods for Domain-Specific Signal Features.
Features to Extract
Mean
— Option to extract mean
false
(default) | true
Option to extract the mean, specified as true
or
false
. If you specify Mean
as true, the
signalTimeFeatureExtractor
object extracts the mean and appends the value to the
features returned by the extract
function.
Data Types: logical
RMS
— Option to extract root mean square
false
(default) | true
Option to extract the root mean square (RMS), specified as true
or false
. If you specify RMS
as true, the
signalTimeFeatureExtractor
object extracts the RMS and appends the value to the
features returned by the extract
function.
Data Types: logical
StandardDeviation
— Option to extract standard deviation
false
(default) | true
Option to extract the standard deviation, specified as true
or
false
. If you specify StandardDeviation
as
true, the signalTimeFeatureExtractor
object extracts the standard deviation and
appends the value to the features returned by the extract
function.
Data Types: logical
ShapeFactor
— Option to extract shape factor
false
(default) | true
Option to extract the shape factor, specified as true
or
false
. The shape factor is equal to the RMS value divided by the
mean absolute value of the signal. If you specify ShapeFactor
as
true, the signalTimeFeatureExtractor
object extracts the shape factor and appends
the value to the features returned by the extract
function.
Data Types: logical
SNR
— Option to extract signal-to-noise ratio
false
(default) | true
Option to extract the signal-to-noise ratio (SNR), specified as
true
or false
. If you specify
SNR
as true, the signalTimeFeatureExtractor
object extracts
the SNR and appends the value to the features returned by the
extract
function.
Data Types: logical
THD
— Option to extract total harmonic distortion
false
(default) | true
Option to extract the total harmonic distortion (THD), specified as
true
or false
. If you specify
THD
as true, the signalTimeFeatureExtractor
object extracts
the THD and appends the value to the features returned by the
extract
function.
Data Types: logical
SINAD
— Option to extract signal to noise and distortion ratio
false
(default) | true
Option to extract the signal to noise and distortion ratio (SINAD) in decibels,
specified as true
or false
. If you specify
Sinad
as true, the signalTimeFeatureExtractor
object extracts
the SINAD and appends the value to the features returned by the
extract
function.
Data Types: logical
PeakValue
— Option to extract peak value
false
(default) | true
Option to extract the peak value, specified as true
or
false
. The peak value corresponds to the maximum absolute value
of the signal. If you specify PeakValue
as true, the
signalTimeFeatureExtractor
object extracts the peak and appends the value to the
features returned by the extract
function.
Data Types: logical
CrestFactor
— Option to extract crest factor
false
(default) | true
Option to extract the crest factor, specified as true
or
false
. The crest factor is equal to the peak value divided by the
RMS. If you specify CrestFactor
as true, the
signalTimeFeatureExtractor
object extracts the crest factor and appends the value
to the features returned by the extract
function.
Data Types: logical
ClearanceFactor
— Option to extract clearance factor
false
(default) | true
Option to extract the clearance factor, specified as true
or
false
. The clearance factor is equal to the peak value divided by
the squared mean of the square roots of the absolute amplitude. If you specify
ClearanceFactor
as true, the signalTimeFeatureExtractor
object extracts the clearance factor and appends the value to the features returned by
the extract
function.
Data Types: logical
ImpulseFactor
— Option to extract impulse factor
false
(default) | true
Option to extract the impulse factor, specified as true
or
false
. The impulse factor is equal to the peak value divided by
the mean of the absolute amplitude. If you specify ImpulseFactor
as true, the signalTimeFeatureExtractor
object extracts the impulse factor and
appends the value to the features returned by the extract
function.
Data Types: logical
Object Functions
extract | Extract time-domain, frequency-domain, or time-frequency-domain features |
generateMATLABFunction | Create MATLAB function compatible with C/C++ code generation |
Examples
Extract Time-Domain Features from Data Set
Extract time-domain features from electromyographic (EMG) data for later use in a machine learning workflow to classify forearm motions. The files are available at this location: https://ssd.mathworks.com/supportfiles/SPT/data/MyoelectricData.zip.
This example uses EMG signals collected from the forearms of 30 subjects [1]. The data set consists of 720 files. Each subject participated in four testing sessions, and performed six trials of different forearm motions per session. Download and unzip the files into your temporary directory.
localfile = matlab.internal.examples.downloadSupportFile( ... "SPT","data/MyoelectricData.zip"); datasetFolder = fullfile(tempdir,"MyoelectricData"); unzip(localfile,datasetFolder)
Each file contains an eight-channel EMG signal that represents the activation of eight forearm muscles during a series of motions. The sample rate is 1000 Hz. Create a signalDatastore
object that points to the data set folder.
fs = 1000; sds = signalDatastore(datasetFolder,IncludeSubfolders=true);
For this example, analyze only the last (sixth) trial of the third session. Use the endsWith
function to find the indices that correspond to these files. Create a new datastore that contains this subset of signals.
idSession = 3; idTrial = 6; idSuffix = "S"+idSession+"T"+idTrial+"d.mat"; p = endsWith(sds.Files,idSuffix); sdssub = subset(sds,p);
Create a signalTimeFeatureExtractor
object to extract the mean, root mean square (RMS), and peak values from the EMG signals. Call the extract
function to extract the specified features.
sFE = signalTimeFeatureExtractor(SampleRate=fs, ...
Mean=true,RMS=true,PeakValue=true);
[M,infoFeatures] = extract(sFE,sdssub);
Features = cell2mat(M);
Plot the peak values for the second and eighth EMG channels.
featureName = "PeakValue"; idPeaks = infoFeatures{1}.(featureName); idChannels = [2 8]; Peaks = squeeze(Features(:,idPeaks,idChannels)); bar(Peaks) xlabel("Subject") ylabel(featureName+" EMG (mV)") legend("Channel"+idChannels) title(featureName+" Feature: Session "+idSession+ ... ", Trial "+idTrial)
More About
Scalarization Methods for Domain-Specific Signal Features
To set the scalarization methods for features in time domain, frequency domain, or time-frequency domain, select the domain-specific feature extractor objects and scalarization method specification. Refer to the following table for the list of domain-specific features from which you can extract scalar features.
Feature domain | Feature extractor object | Scalarization method specification | Features that support scalarization |
---|---|---|---|
Time | signalTimeFeatureExtractor | timeScalarFeatureOptions object | PeakValue |
Frequency | signalFrequencyFeatureExtractor | frequencyScalarFeatureOptions object | PeakAmplitude WelchPSD |
Time-frequency | signalTimeFrequencyFeatureExtractor | timeFrequencyScalarFeatureOptions object | All time-frequency features |
For a given feature vector v with N elements, the scalarization method options convert v to a scalar s as follows.
"Mean"
— Mean, defined as the average value of v."StandardDeviation"
— Standard deviation of the elements of v, normalized by N-1."PeakValue"
— Peak value, defined as the maximum absolute value of v."Kurtosis"
— Kurtosis, defined as the ratio between the fourth moment of v and the squared second moment of v."Skewness"
— Skewness, defined as the ratio between the third moment of v and the second moment of v raised to the power of 1.5.
"ClearanceFactor"
— Clearance factor, defined as the ratio between the peak value of v and the squared mean of the square roots of the absolute values of v."CrestFactor"
— Crest factor, defined as the ratio between the peak value of v and the root-mean-square value of v."Energy"
— Energy, defined as the sum of the squared values of v."Entropy"
— Entropy, defined as the sum of plog2p values, where p is the vector of normalized squared values of v with respect to their sum.where
Note
The scalarization method
"Entropy"
is not supported for theWaveletEntropy
nor theSpectralEntropy
features."ImpulseFactor"
— Impulse factor, defined as the ratio between the peak value of v and the average absolute value of v.
References
[1] Chan, Adrian D.C., and Geoffrey C. Green. 2007. "Myoelectric Control Development Toolbox." Paper presented at 30th Conference of the Canadian Medical & Biological Engineering Society, Toronto, Canada, 2007.
Extended Capabilities
C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.
Usage notes and limitations:
You cannot generate code directly from
signalTimeFeatureExtractor
. You can generate C/C++ code from the function returned bygenerateMATLABFunction
.
GPU Arrays
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.
This function fully supports GPU arrays. For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).
Version History
Introduced in R2021aR2024b: Specify scalarization methods for time-domain signal features
The signalTimeFeatureExtractor
function supports specifying methods to extract
scalar features in time domain.
R2023a: Use gpuArray
inputs
The signalTimeFeatureExtractor
object supports gpuArray
inputs. You
must have Parallel Computing Toolbox™ to use this functionality.
See Also
Functions
Objects
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