studies utilized the EMD for various applications. In Nunes et al. [8] used the EMD
for texture analysis and image filtering. They have used bi-dimensional EMD in
their method. In another work Zeng et al. [9] applied EMD for the effective classification of gait patterns between patients with Parkinson disease and healthy subjects. In another work Hasan et al. [10] combined the deep learning methods with
EMD to classify cardiovascular disease. Xiwei et al. [11] utilized the advantages of
EMD in a wind speed prediction model, in which, authors used EMD for the
extraction of fluctuation features of wind speed data. Another important study by
Thilagaraj et al. [12] also used EMD for the identification of alcoholism.
The usefulness of the empirical mode composition for the effective understanding of the EEG signal is proven in many works in the literature. In [13], authors
classified the level of autism severity from EEG with the help of EMD. They have
used artificial neural network for the classification of extracted feature from intrinsic mode functions (IMFs). Two-class motor imagery EEG signals are classified in
another important study based on EMD [14]. Similarly Gaur et al. [15] used multivariate empirical mode decomposition for the effective classification of multi-class
BCI by analyzing EEG signals.
In this work, we have studied the effectiveness of empirical mode decomposition for the classification of seizures by analyzing EEG signals. The filtered EEG
signals are segmented into 10 non-overlapping segments and decomposed into
IMFs using EMD. First four IMFs are used for the feature extraction. Various
features such as approximate entropy, sample entropy, Shannon entropy, Rényi
entropy, exponential energy, fractal dimensional features and statistical features
(mean, standard deviation and energy) are extracted from the IMFs. Support vector
machine (SVM) with RBF kernel is used for classifying the seizure.
The remaining sections of the paper are as follows. A short description of EMD
and algorithm is explained in Section 2. Section 3 explains the details of the dataset
used in this study and in Section 4; various feature extraction methods are mentioned. In Section 5 experimental setup and results are explained. A detailed discussion of achieved results is given in Section 6 and Section 7 concludes the paper.
2. Empirical mode decomposition (EMD)
Empirical mode decomposition is a data-driven decomposition method proposed
by Huang et al. for the analysis of nonlinear and non-stationary data [7], which will
decomposes the signal into finite and smaller number of intrinsic mode functions
(IMFs). A non-stationary signal can be represented as sum of IMFs and each IMFs
should follow two conditions: (1) the number of extrema and number of zero
crossing of the IMFs should be equal or differ at most by one and (2) the mean value
of two envelopes defined by local maxima and local minima should be zero [16].
IMFs can be extracted from a signal through a iterative method known as
shifting process as follows:
1. Use cubic spline interpolation method to construct upper (e max ) and lower
(e min ) envelops by connecting detected maxima and minima individually from
the signal x(t).
2. Calculate the mean mt ðÞ¼
e max þe min
ðÞ
2
.
3. Extract the difference d(t) between signal x(t) and calculated m 1 (t),
dt ðÞ¼xt ðÞ�mt ðÞ .
66
Advances in Neural Signal Processing
for texture analysis and image filtering. They have used bi-dimensional EMD in
their method. In another work Zeng et al. [9] applied EMD for the effective classification of gait patterns between patients with Parkinson disease and healthy subjects. In another work Hasan et al. [10] combined the deep learning methods with
EMD to classify cardiovascular disease. Xiwei et al. [11] utilized the advantages of
EMD in a wind speed prediction model, in which, authors used EMD for the
extraction of fluctuation features of wind speed data. Another important study by
Thilagaraj et al. [12] also used EMD for the identification of alcoholism.
The usefulness of the empirical mode composition for the effective understanding of the EEG signal is proven in many works in the literature. In [13], authors
classified the level of autism severity from EEG with the help of EMD. They have
used artificial neural network for the classification of extracted feature from intrinsic mode functions (IMFs). Two-class motor imagery EEG signals are classified in
another important study based on EMD [14]. Similarly Gaur et al. [15] used multivariate empirical mode decomposition for the effective classification of multi-class
BCI by analyzing EEG signals.
In this work, we have studied the effectiveness of empirical mode decomposition for the classification of seizures by analyzing EEG signals. The filtered EEG
signals are segmented into 10 non-overlapping segments and decomposed into
IMFs using EMD. First four IMFs are used for the feature extraction. Various
features such as approximate entropy, sample entropy, Shannon entropy, Rényi
entropy, exponential energy, fractal dimensional features and statistical features
(mean, standard deviation and energy) are extracted from the IMFs. Support vector
machine (SVM) with RBF kernel is used for classifying the seizure.
The remaining sections of the paper are as follows. A short description of EMD
and algorithm is explained in Section 2. Section 3 explains the details of the dataset
used in this study and in Section 4; various feature extraction methods are mentioned. In Section 5 experimental setup and results are explained. A detailed discussion of achieved results is given in Section 6 and Section 7 concludes the paper.
2. Empirical mode decomposition (EMD)
Empirical mode decomposition is a data-driven decomposition method proposed
by Huang et al. for the analysis of nonlinear and non-stationary data [7], which will
decomposes the signal into finite and smaller number of intrinsic mode functions
(IMFs). A non-stationary signal can be represented as sum of IMFs and each IMFs
should follow two conditions: (1) the number of extrema and number of zero
crossing of the IMFs should be equal or differ at most by one and (2) the mean value
of two envelopes defined by local maxima and local minima should be zero [16].
IMFs can be extracted from a signal through a iterative method known as
shifting process as follows:
1. Use cubic spline interpolation method to construct upper (e max ) and lower
(e min ) envelops by connecting detected maxima and minima individually from
the signal x(t).
2. Calculate the mean mt ðÞ¼
e max þe min
ðÞ
2
.
3. Extract the difference d(t) between signal x(t) and calculated m 1 (t),
dt ðÞ¼xt ðÞ�mt ðÞ .
66
Advances in Neural Signal Processing