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# hht

Hilbert-Huang transform

## Syntax

``hs = hht(imf,fs)``
``[hs,f,t] = hht(imf,fs)``
``[hs,f,t,imfinsf,imfinse] = hht(___)``
``[___] = hht(___,Name,Value)``
``hht(___)``
``hht(___,freqlocation)``

## Description

````hs = hht(imf,fs)` returns the Hilbert spectrum `hs` of the signal specified by intrinsic mode functions `imf`, and the optional sampling frequency `fs`. `hs` is useful for analyzing signals that comprise a mixture of signals whose spectral content changes in time. Use `hht` to perform Hilbert spectral analysis on signals to identify localized features.```
````[hs,f,t] = hht(imf,fs)` returns frequency vector `f` and time vector `t` in addition to `hs`.```

example

````[hs,f,t,imfinsf,imfinse] = hht(___)` also returns the instantaneous frequencies `imfinsf` and the instantaneous energies `imfinse` of the intrinsic mode functions for signal diagnostics.```
````[___] = hht(___,Name,Value)` estimates Hilbert spectrum parameters with additional options specified by one or more `Name,Value` pair arguments.```
````hht(___)` with no output arguments plots the Hilbert spectrum in the current figure window. You can use this syntax with any of the input arguments in previous syntaxes.```

example

````hht(___,freqlocation)` plots the Hilbert spectrum with the optional `freqlocation` argument to specify the location of the frequency axis. Frequency is represented on the y-axis by default.```

## Examples

collapse all

For this example, consider a nonstationary continuous signal composed of sinusoidal waves with a distinct change in frequency. The vibration of a jackhammer and the sound of fireworks are examples of nonstationary continuous signals.

Load the nonstationary signal data with the sampling frequency `fs`, and visualize the mixed sinusoidal signal.

```load('sinusoidalSignalExampleData.mat','X','fs') t = (0:length(X)-1)/fs; plot(t,X) xlabel('Time(s)')``` The mixed signal contains sinusoidal waves with different amplitude and frequency values.

To create the Hilbert spectrum plot, you need the intrinsic mode functions (IMFs) of the signal. Perform empirical mode decomposition to compute the IMFs and residuals of the signal. Since the signal is not smooth, specify '`pchip`' as the `Interpolation` method.

`[imf,residual,info] = emd(X,'Interpolation','pchip');`
```Current IMF | #Sift Iter | Relative Tol | Stop Criterion Hit 1 | 2 | 0.026352 | SiftMaxRelativeTolerance 2 | 2 | 0.0039573 | SiftMaxRelativeTolerance 3 | 1 | 0.024838 | SiftMaxRelativeTolerance 4 | 2 | 0.05929 | SiftMaxRelativeTolerance 5 | 2 | 0.11317 | SiftMaxRelativeTolerance 6 | 2 | 0.12599 | SiftMaxRelativeTolerance 7 | 2 | 0.13802 | SiftMaxRelativeTolerance 8 | 3 | 0.15937 | SiftMaxRelativeTolerance 9 | 2 | 0.15923 | SiftMaxRelativeTolerance The decomposition stopped because the number of extrema of the residual signal is less than 'MaxNumExtrema'. ```

The table generated in the command window indicates the number of sift iterations, the relative tolerance, and the sift stop criterion for each generated IMF. This information is also contained in `info`. You can hide the table by adding the `'Display',0` name value pair.

Create the Hilbert spectrum plot using the `imf` components obtained using empirical mode decomposition.

`hht(imf,fs)` The frequency versus time plot is a sparse plot with a vertical color bar indicating the instantaneous energy at each point in the IMF. The plot represents the instantaneous frequency spectrum of each component decomposed from the original mixed signal.Three IMFs appear in the plot with a distinct change in frequency at 1 second.

For this example, consider a nonstationary continuous signal composed of sinusoidal waves with a distinct change in frequency. The vibration of a jackhammer and the sound of fireworks are examples of nonstationary continuous signals.

Load the nonstationary signal data with the sampling frequency `fs`, and visualize the mixed sinusoidal signal.

```load('sinusoidalSignalExampleData.mat','X','fs') t = (0:length(X)-1)/fs; plot(t,X) xlabel('Time(s)')``` The mixed signal contains sinusoidal waves with different amplitude and frequency values.

To compute the Hilbert spectrum parameters, you need the IMFs of the signal. Perform empirical mode decomposition to compute the intrinsic mode functions and residuals of the signal. Since the signal is not smooth, specify 'pchip' as the Interpolation method.

`[imf,residual,info] = emd(X,'Interpolation','pchip');`
```Current IMF | #Sift Iter | Relative Tol | Stop Criterion Hit 1 | 2 | 0.026352 | SiftMaxRelativeTolerance 2 | 2 | 0.0039573 | SiftMaxRelativeTolerance 3 | 1 | 0.024838 | SiftMaxRelativeTolerance 4 | 2 | 0.05929 | SiftMaxRelativeTolerance 5 | 2 | 0.11317 | SiftMaxRelativeTolerance 6 | 2 | 0.12599 | SiftMaxRelativeTolerance 7 | 2 | 0.13802 | SiftMaxRelativeTolerance 8 | 3 | 0.15937 | SiftMaxRelativeTolerance 9 | 2 | 0.15923 | SiftMaxRelativeTolerance The decomposition stopped because the number of extrema of the residual signal is less than 'MaxNumExtrema'. ```

The table generated in the command window indicates the number of sift iterations, the relative tolerance, and the sift stop criterion for each generated IMF. This information is also contained in `info`. You can hide the table by specifying `Display` as `0`.

Compute the Hilbert spectrum parameters: Hilbert spectrum `hs`, frequency vector `fs`, time vector `t`, instantaneous frequency `imfinsf`, and instantaneous energy `imfinse`.

`[hs,f,t,imfinsf,imfinse] = hht(imf,fs);`

Use the computed Hilbert spectrum parameters for time-frequency analysis and signal diagnostics.

## Input Arguments

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Intrinsic mode function, specified as a matrix or timetable. `imf` is any signal whose envelope is symmetric with respect to zero and whose numbers of extrema and zero crossings differ by at most one. `emd` is used to decompose and simplify complicated signals into a finite number of intrinsic mode functions required to perform Hilbert spectral analysis.

`hht` treats each column in `imf` as an intrinsic mode function. For more information on computing `imf`, see `emd`.

Sampling frequency, specified as a positive scalar. Sampling frequency or sampling rate is the number of samples per unit time.

If `fs` is not supplied, a normalized frequency of `2π` is used to compute the Hilbert spectrum. If `imf` is specified as a timetable, the sampling time is inferred from it.

Location of frequency axis on the plot, specified as `'yaxis'` or `'xaxis'`. To display frequency data on the y-axis or x-axis of the plot, specify `freqlocation` as `'yaxis'` or `'xaxis'` respectively.

### Name-Value Pair Arguments

Specify optional comma-separated pairs of `Name,Value` arguments. `Name` is the argument name and `Value` is the corresponding value. `Name` must appear inside quotes. You can specify several name and value pair arguments in any order as `Name1,Value1,...,NameN,ValueN`.

Example: `'FrequencyResolution',1`

Frequency limits to compute Hilbert spectrum, specified as the comma-separated pair consisting of '`FrequencyLimits`' and a 1-by-2 integer-valued vector. `FrequencyLimits` is specified in Hz.

Frequency resolution to discretize frequency limits, specified as the comma-separated pair consisting of '`FrequencyResolution`' and a positive scalar.

Specify `FrequencyResolution` in Hz. If `'FrequencyResolution'` is not specified, a value of `(f_high-f_low)/100` is inferred from `FrequencyLimits`. Here, `f_high` is the upper limit of `FrequencyLimits`, while `f_low` is the lower limit.

Minimum threshold value of Hilbert spectrum, specified as the comma-separated pair consisting of '`MinThreshold`' and a scalar.

`MinThreshold` sets elements of `hs` to 0 when the corresponding elements of $10{\mathrm{log}}_{10}\left(hs\right)$ are less than `MinThreshold`.

## Output Arguments

collapse all

Hilbert spectrum of the signal, returned as a sparse array. Use `hs` for time-frequency analysis and to identify localized features in the signal.

Frequency vector of the signal, returned as a numeric array. `hht` uses the frequency vector `f` and the time vector `t` to create the Hilbert spectrum plot.

Mathematically, `f` is denoted as:

`f = f_low:FrequencyResolution:f_high`

Time vector of the signal, returned as a numeric array or a `duration` array. `hht` uses the time vector `t` and the frequency vector `f` to create the Hilbert spectrum plot.

`t` is returned as:

• An array, if `imf` is specified as an array.

• A `duration` array, if `imf` is specified as a uniformly sampled timetable.

Instantaneous frequency of each IMF, returned as an array or timetable.

`insf` has the same number of columns as `imf` and is returned as:

• An array, if `imf` is specified as an array.

• A timetable, if `imf` is specified as a uniformly sampled timetable.

Instantaneous energy of each IMF, returned as an array or timetable.

`inse` has the same number of columns as `imf` and is returned as:

• An array, if `imf` is specified as an array.

• A timetable, if `imf` is specified as a uniformly sampled timetable.

## Algorithms

The Hilbert-Huang transform is useful for performing time-frequency analysis of nonstationary and nonlinear data. The Hilbert-Huang procedure consists of the following steps:

1. `emd` decomposes the data set x into a finite number of intrinsic mode functions.

2. For each intrinsic mode function, xi, the function `hht`:

1. Uses `hilbert` to compute the analytic signal, ${z}_{i}\left(t\right)={x}_{i}\left(t\right)+jH\left\{{x}_{i}\left(t\right)\right\}$, where H{xi} is the Hilbert transform of xi.

2. Expresses zi as ${z}_{i}\left(t\right)={a}_{i}\left(t\right)\text{\hspace{0.17em}}{e}^{j{\theta }_{i}\left(t\right)}$, where ai(t) is the instantaneous amplitude and ${\theta }_{i}\left(t\right)$ is the instantaneous phase.

3. Computes the instantaneous energy, $|{a}_{i}\left(t\right){|}^{2}$, and the instantaneous frequency, ${\omega }_{i}\left(t\right)\equiv d{\theta }_{i}\left(t\right)/dt$. If given a sample rate, `hht` converts ${\omega }_{i}\left(t\right)$ to a frequency in Hz.

4. Outputs the instantaneous energy in `imfinse` and the instantaneous frequency in `imfinsf`.

3. When called with no output arguments, `hht` plots the energy of the signal as a function of time and frequency, with color proportional to amplitude.

 Huang, Norden E., and Samuel S. P. Shen. Hilbert-Huang Transform and Its Applications. Singapore: World Scientific, 2014.

 Huang, Norden E., Zhaohua Wu, Steven R. Long, Kenneth C. Arnold, Xianyao Chen, and Karin Blank. "On Instantaneous Frequency." Advances in Adaptive Data Analysis. Vol. 1, No. 2, 2009, pp. 177–229.