Ffeature extraction of epilepsy eeg using discrete wavelet transform
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Transcript of 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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The Proposed ApproachFeature Extraction using DWT
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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The Proposed ApproachFeature Extraction using DWT
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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The Proposed ApproachFeature Extraction using DWT
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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Experimental Results
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Approximate and coefficients are taken from an epileptic subject (set D).
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Experimental Results
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Approximate and coefficients are taken from an epileptic subject (set E).
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Experimental Results
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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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Experimental Results
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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
Max MinMeanMedianVarianceDeviation Energy RWEEntropy Skewness
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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Experimental Results
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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
Max MinMeanMedianVarianceDeviation Energy RWEEntropy Skewness
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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Experimental Results
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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