ffeature extraction of epilepsy eeg using discrete wavelet transform

Feature Extraction of Epilepsy EEG using Discrete Wavelet Transform

Asmaa Hamad Elsaied

ICENCO Cairo 2016, Cairo University (29-December-2016)

Master Student, Faculty of Computers and Information, Mina University

Introduction Related Work Materials and Methods

EEG Data Acquisition Discrete wavelet transforms (DWT)

Proposed Approach Results and Discussion Conclusion and future work

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Agenda

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Introduction Epilepsy is one of the most common a chronic

neurological disorders of the brain that affect millions of the world’s populations. It is characterized by recurrent seizures, which are

physical reactions to sudden, usually brief, excessive electrical discharges in a group of brain cells. Hence, seizure identification has great importance in clinical therapy of epileptic patients.

Electroencephalogram (EEG) is most commonly used in epilepsy detection since it includes precious physiological information of the brain.

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Introduction Electroencephalogram (EEG)

The EEG signal is usually used for the

purpose of recording the electrical

activities of the brain signal that

typically arises in the human brain.

The recording of the electrical activity is

basically done by placing electrodes on

the scalp, which measures the voltage

fluctuations in the brain.

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EEG contains lots of worthy information relating

to the numerous physiological states of the brain

and thus is a very useful tool for understanding

the brain disease, such as epilepsy.

EEG signals of epileptic patients exhibit two

states of abnormal activities namely interictal or

seizure free (in-between epileptic seizures) and

ictal (in the course of an epileptic seizure).

Introduction Electroencephalogram (EEG) sub-bands

The EEG signals are commonly

decomposed into five EEG sub-

bands:

delta, theta, alpha, beta and

gamma.

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Introduction Electroencephalogram (EEG)

Frequency range and amplitude for each type of waves

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Wave Frequency range Amplitude

Delta band 0.5 – 4 Hz High

Theta band 4 – 8 Hz Low-medium

Alpha band 8 – 15 Hz Low

Beta band 15 – 30 Hz Very low

Gamma band 30 – 60 Hz Smallest

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Related WorkRef. Method # of Features Remark[8] EMD Eight features The EMD generates the set of amplitude and frequency modulated components known as intrinsic

mode functions (IMFs).Two area measures have been computed, one for the graph obtained as the analytic signal representation of IMFs in the complex plane and another for second-order difference plot (SODP) of IMFs of EEG signals. Both of these area measures have been computed for first four IMFs of the normal and epileptic seizure EEG signals. These eight features obtained from both area measures of first four IMFs have been used as input feature set for classification of normal and epileptic seizure EEG signals using least square support vector machine (LS-SVM) classifier.

[9] DWT Four features The proposed technique involved training the ANFIS classifier to detect epileptic seizure in EEG when the statistical features extracted from the wavelet sub-bands of EEG signals were used as inputs

[10] DWT Four features Proposed a wavelet-based feature extraction technique which consequently uses simple statistical parameters to detect epileptic EEG signals using a back propagating artificial neural network classifier.

[11] DWT One feature Approximate Entropy (ApEn) for epilepsy detection from EEG signals is used.[12] ICA

Three features Improving the accuracy of EEG signal classification is presented to detect epileptic seizures. ICA is

incorporated as a preprocessing step and Short-Time Fourier Transform (STFT) is used for de-noising the signal adequately.

[13] ICA four features The signals were decomposed into the frequency sub-bands using DWT and a set of statistical features was extracted from the subbands to represent the distribution of wavelet coefficients. Principal components analysis (PCA), independent components analysis (ICA) and linear discriminant analysis (LDA) is used to reduce the dimension of data. Then these features were used as an input to a support vector machine (SVM) with two discrete outputs: epileptic seizure or not.

[14] DWT Four features Original EEG signals representation by wavelet packet coefficients and feature extraction using the best basis-based wavelet packet entropy method.

[15] DWT Four features Each EEG signal is decomposed into five constituent EEG sub-bands by DWT. The nonlinear parameters of each sub-band and the original EEG are quantified in the form of the time lag (TL), the embedding dimension (ED), the correlation dimension (CD), and the largest Lyapunov exponent (LLE).

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fewer previous research on the feature extraction methods in EEG signals

EEG Data Sets The experimental data used is publically available

Bonn data set “Klinik für Epileptologie, Universität Bonn’’. The dataset includes five different sets:

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•5 awake healthy subjects with eyes open•Surface EEG recording

A

•5 awake healthy subjects with eyes closed•Surface EEG recording

B

•Inter-ictal EEG from five epileptic patients•intracranial depth electrodes from hippocampal formation of opposite hemisphere the

brain

C

•Inter-ictal EEG from five epileptic patients •Intracranial depth electrodes from epileptogenic zone.

D

•Ictal EEG from five epileptic patients•depth and strip electrodes

E

EEG Data Samples

EEG signals of each dataset.

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The EEG signals of three subsets namely A, D, and E have been used.

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EEG Data Sets Characterstics

Settings Set A Set D Set ESubjects 5 healthy 5 epileptic

patients5 epileptic patients

Electrode type surface Intracranial IntracranialElectrode placement

International 10-20 system

Hippocampal formation

Epileptogenic zone

Patient’s state Awake, eyes open

Seizure-free (Interictal)

Seizure activity (Ictal)

Number of epochs

100 100 100

Epoch duration (s)

23.6 23.6 23.6

Discrete wavelet transforms (DWT)

A wavelet is a short wave, which has its energy intensified in time to give a tool for the analysis of transient, non-stationary signals or time-varying phenomena .

If a signal does not change much over time, we would call it as a stationary signal.

Fourier transform could be applied to the stationary signals easily and a good result can be taken.

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Discrete wavelet transforms (DWT)

However, many signals like EEG having the non-stationary and transient characteristics, in such situation ideally Fourier transform may not be applied directly. But time–frequency methods can be used .

Wavelet transform method has been used to extract the individual EEG sub-bands and reconstruct the information accurately because the wavelet transform has the advantages of: time-frequency localization, multi-rate filtering, and scale-space analysis.

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The Proposed Approach (General model)Identify the epileptic seizure

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The Proposed ApproachFeature Extraction using DWT

Each EEG signal is decomposed into five constituent EEG sub-bands by discrete wavelet transform (DWT).

The EEG epochs were analyzed into various frequency bands by using fourth-order Daubechies (db4) wavelet function up to 4th-level of the decomposition. The statistical parameter like entropy, min, max, mean, median, standard deviation, variance, energy and Relative Wave Energy (RWE) were computed for feature extraction.

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Feature extraction is a special form of dimensionality reduction. When the input data to an algorithm is too large to be processed and it is suspected to be notoriously redundant (much data, but not much information) then the input data will be transformed into a reduced representation set of features (also named features vector). Transforming the input data into the set of features is called feature extraction.

If the features extracted are carefully chosen it is expected that the features set will extract the relevant information from the input data in order to perform the desired task using this reduced representation instead of the full size input.

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Following features of wavelet coefficients from each sub band that were extracted to classify EEG signals. Maximum of the wavelet coefficients in each sub-band. Minimum of the wavelet coefficients in each sub-band. Mean of the wavelet coefficients in each sub-band The standard deviation of the wavelet coefficients in

each sub-band. The variance of the wavelet coefficients in each sub-

band is the square of the standard deviation. The median of the wavelet coefficients in each sub-

band.

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Skewness of the wavelet coefficients in each sub-band. A measure of the asymmetry of the data distribution. Energy in the sub-band

The energy points out that the strength of the signal as it gives the area under the curve of power at any interval of time.

Relative Wave Energy (RWE) in the sub-bandRWE characterize the relative energy in each frequency sub-band and is utilize to detect the correspondence between segments of EEG signal.

Entropy in the sub-band.Entropy is a numerical measure of uncertainty (doubt) of outcome where signal contained thousands of bits of information.

Based on the above mentioned, ten features were extracted for chosen categories of signals to create the original feature database at each decomposition level starting from D1–D4 and one final approximation, A4. These are extracted to help in distinguishing between normal and epileptic signal.

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Decomposition level

Sub-band signal

Frequency band (Hz)

1 D1(gamma) 30-602 D2 (beta) 15-303 D3 (alpha) 8-154 D4 (theta) 4-84 A4 (delta) 0-4

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Experimental Results

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Approximate and coefficients are taken from a healthy subject (set A).

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Approximate and coefficients are taken from an epileptic subject (set D).

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Approximate and coefficients are taken from an epileptic subject (set E).

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EXTRACTED FEATURE COEFFICIENTS FOR THE LAST EPOCH OF SET A

Set Features

Wavelet Sub-bandsD1 (Gamma) D2 (Beta) D3 (Alpha) D4 (Theta) A4 (Delta)

Set A

Max MinMeanMedianVarianceDeviation Energy RWEEntropy Skewness

9.946874e-04-9.265823e-04-1.269325e-073.465760e-066.455155e-082.540700e-041.323953e-048.794600e-06-2.087377e-033.727474e-01

7.879125e-03-8.628986e-03-1.483079e-06-4.923745e-055.112694e-062.261127e-03 5.255851e-033.491297e-04-5.988027e-024.730014e-01

3.901077e-02-4.608552e-02-1.217418e-04-1.900916e-041.369548e-041.170277e-027.081328e-024.703904e-03-5.705388e-07.493647e-01

7.471598e-02-7.253425e-021.033281e-03-5.439984e-046.086433e-042.467070e-021.591356e-011.057088e-02-1.056781e+002.714428e-02

6.104268e-01-8.788146e-01-7.732445e-03-1.336441e-025.671705e-022.381534e-011.481882e+019.843673e-01-2.977001e+016.492470e-01

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EXTRACTED FEATURE COEFFICIENTS FOR THE LAST EPOCH OF SET D

Set Extracted Features

Wavelet Sub-bandsD1 (Gamma)

D2 (Beta) D3 (Alpha)

D4 (Theta)

A4 (Delta)

Set D


3.969254e-04-3.742630e-04-1.062821e-07-3.011566e-067.481376e-098.649495e-051.534432e-052.407420e-06-2.750434e-044.457542e-01

4.264739e-03-4.189743e-032.729993e-062.262996e-055.425604e-077.365870e-045.577597e-048.750869e-05-7.531945e-032.173789e+00

1.840372e-02-2.153365e-02-5.010535e-05-4.831392e-052.228588e-054.720792e-031.152310e-021.807896e-03-1.118578e-012.088849e+00

7.366416e-02-8.237293e-021.173667e-045.086258e-045.202897e-042.280986e-021.357992e-012.130597e-02-8.970721e-011.364383e+00

3.685789e-01-4.581400e-01-7.577910e-03-1.029943e-022.379626e-021.542604e-016.225868e+009.767962e-01-1.877158e+01-2.049270e-01

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EXTRACTED FEATURE COEFFICIENTS FOR THE LAST EPOCH OF SET E

Set Extracted Features

Wavelet Sub-bandsD1 (Gamma) D2 (Beta) D3 (Alpha) D4 (Theta) A4 (Delta)

Set E


3.955192e-03-3.549488e-036.948730e-07-2.612595e-057.854200e-078.862392e-041.610897e-031.645268e-05-2.096376e-021.662602e+00

3.189520e-02-2.833828e-02-5.382679e-06-3.217398e-049.073279e-059.525376e-039.327334e-029.526346e-04-7.912125e-012.449215e-01

1.880762e-01-1.478077e-011.642593e-03-4.622447e-033.228821e-035.682271e-021.670698e+001.706345e-02-8.436742e+00-2.552237e-01

5.564677e-01-7.536238e-011.904032e-04-9.030940e-037.326989e-022.706841e-011.912345e+011.953148e-01-4.057527e+01-9.135021e-01

2.277090e+00-2.324888e+003.123408e-031.971368e-032.950933e-015.432249e-017.702190e+017.866527e-01-8.908658e+002.659280e+00

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Conclusion

An improved DWT technique to extract ten features from EEG signal in

which can be used to classify epileptic seizure.

The number of features extracted using the improved DWT has considered

the best when compared to other studies as have demonstrated in related

work section.

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Future work

We plan to select the significant and relevant features from these huge

number of features based on swarm optimization algorithms like Whale

Optimization Algorithm (WOA).

then ANN is used for the classification, which it can be easily distinguished

between normal and epileptic.

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Thanks and Acknowledgement27

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ffeature extraction of epilepsy eeg using discrete wavelet transform

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