# 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`

= signalTimeFeatureExtractor`signalTimeFeatureExtractor`

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`

, the`signalTimeFeatureExtractor`

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 to`true`

.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`

, the`signalTimeFeatureExtractor`

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.

**All Signal Domains**

`"Mean"`

— Mean, defined as the average value of*v*.$$s=\overline{v}=\frac{1}{N}{\displaystyle \sum _{i=1}^{N}{v}_{i}}$$

`"StandardDeviation"`

— Standard deviation of the elements of*v*, normalized by*N*-1.$$s=\sqrt{\frac{1}{N-1}{\displaystyle \sum _{i=1}^{N}|{v}_{i}-\overline{v}{|}^{2}}}$$

`"PeakValue"`

— Peak value, defined as the maximum absolute value of*v*.$$s={v}_{p}=\underset{i}{\mathrm{max}}\left|{v}_{i}\right|$$

`"Kurtosis"`

— Kurtosis, defined as the ratio between the fourth moment of*v*and the squared second moment of*v*.$$s=\frac{\frac{1}{N}{\displaystyle \sum _{i=1}^{N}{({v}_{i}-\overline{v})}^{4}}}{{\left[\frac{1}{N}{\displaystyle \sum _{i=1}^{N}{({v}_{i}-\overline{v})}^{2}}\right]}^{2}}$$

`"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.$$s=\frac{\frac{1}{N}{\displaystyle \sum _{i=1}^{N}{({v}_{i}-\overline{v})}^{3}}}{{\left[\frac{1}{N}{\displaystyle \sum _{i=1}^{N}{({v}_{i}-\overline{v})}^{2}}\right]}^{3/2}}$$

**Frequency and Time-Frequency Signal Domains**

`"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*.$$s=\frac{{v}_{p}}{{\left(\frac{1}{N}{\displaystyle \sum _{i=1}^{N}\sqrt{\left|{v}_{i}\right|}}\right)}^{2}}$$

`"CrestFactor"`

— Crest factor, defined as the ratio between the peak value of*v*and the root-mean-square value of*v*.$$s=\frac{{v}_{p}}{\sqrt{\frac{1}{N}{\displaystyle \sum _{i=1}^{N}{v}_{i}{}^{2}}}}$$

`"Energy"`

— Energy, defined as the sum of the squared values of*v*.$$s={\displaystyle \sum _{i=1}^{N}{v}_{i}{}^{2}}$$

`"Entropy"`

— Entropy, defined as the sum of*p*log_{2}*p*values, where*p*is the vector of normalized squared values of*v*with respect to their sum.$$s={\displaystyle \sum _{i=1}^{N}{p}_{i}{\mathrm{log}}_{2}{p}_{i}},$$

where

$$p=\frac{{v}^{2}}{{\displaystyle \sum _{i=1}^{N}{v}_{i}{}^{2}}}.$$

**Note**The scalarization method

`"Entropy"`

is not supported for the`WaveletEntropy`

nor the`SpectralEntropy`

features.`"ImpulseFactor"`

— Impulse factor, defined as the ratio between the peak value of*v*and the average absolute value of*v*.$$s=\frac{{v}_{p}}{\frac{1}{N}{\displaystyle \sum _{i=1}^{N}\left|{v}_{i}\right|}}$$

## 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 by`generateMATLABFunction`

.

### 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 R2021a**

### R2024b: 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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