Chapter 5
Empirical Mode Decomposition of
EEG Signals for the Effectual
Classification of Seizures
Fasil OK and Reghunadhan Rajesh
Abstract
Empirical mode decomposition (EMD) is a remarkable method for the analysis
of nonlinear and non-stationary data. EMD will breakdown the given signal into
intrinsic mode functions (IMFs), which can represent natural signals effectively. In
this work, the competence of EMD with traditional features to classify the seizure
and non-seizure EEG signals is studied. Due to the complex nature of human brain,
the EEG signals which are recorded from different regions of brain are nonstationary in nature. Different features such as entropy features (approximate
entropy (ApEn), sample entropy (SmEn), Shannon entropy (ShEn), Rényi entropy
(RnEn)), fractal dimension features (Petrosian fractal dimension, Higuchi fractal
dimension, Katz fractal dimension), statistical features (mean, standard deviation
and energy) and exponential energy features are extracted from IMFs and fed to a
SVM classifier. The performances of extracted features are studied independently.
The result shows that, the EMD method is well suited for complex seizure EEG
signal classification.
Keywords: seizures, EEG, empirical mode decomposition, intrinsic mode functions
1. Introduction
Seizures are characterized as unexpected, unprovoked and uncontrolled explosion
of electrical impulses in brain [1]. During the seizure, the patient may experiences
changes in behavior, loss of consciousness, unusual movements and unusual feelings
[2, 3]. The recurrent and unprovoked seizure leads to epilepsy disorder which is a
prevalent neurological disorder. Epilepsy disorder will tamper the patients way of life
with social stigma, work productivity lose and premature death [4].
Electroencephalogram (EEG) is one of the traditional and easiest tool for the
identification and diagnosis of seizures [5]. The availability of EEG for common
people within their budgetary limits made it a typical method. Due to the sophisticated nature of brain system, the EEG signals acquired from the brain are also
complicated. Automated analysis of EEG signals using modern signal processing
techniques might be effortless and precise for the diagnosis of seizures rather than
manual approach [6].
Out of modern signal processing techniques, empirical mode decomposition
(EMD) is one of the widely used techniques for the efficient interpretation of
signals and images. After the introduction of EMD by Huang [7] in 1998, several
65
Empirical Mode Decomposition of
EEG Signals for the Effectual
Classification of Seizures
Fasil OK and Reghunadhan Rajesh
Abstract
Empirical mode decomposition (EMD) is a remarkable method for the analysis
of nonlinear and non-stationary data. EMD will breakdown the given signal into
intrinsic mode functions (IMFs), which can represent natural signals effectively. In
this work, the competence of EMD with traditional features to classify the seizure
and non-seizure EEG signals is studied. Due to the complex nature of human brain,
the EEG signals which are recorded from different regions of brain are nonstationary in nature. Different features such as entropy features (approximate
entropy (ApEn), sample entropy (SmEn), Shannon entropy (ShEn), Rényi entropy
(RnEn)), fractal dimension features (Petrosian fractal dimension, Higuchi fractal
dimension, Katz fractal dimension), statistical features (mean, standard deviation
and energy) and exponential energy features are extracted from IMFs and fed to a
SVM classifier. The performances of extracted features are studied independently.
The result shows that, the EMD method is well suited for complex seizure EEG
signal classification.
Keywords: seizures, EEG, empirical mode decomposition, intrinsic mode functions
1. Introduction
Seizures are characterized as unexpected, unprovoked and uncontrolled explosion
of electrical impulses in brain [1]. During the seizure, the patient may experiences
changes in behavior, loss of consciousness, unusual movements and unusual feelings
[2, 3]. The recurrent and unprovoked seizure leads to epilepsy disorder which is a
prevalent neurological disorder. Epilepsy disorder will tamper the patients way of life
with social stigma, work productivity lose and premature death [4].
Electroencephalogram (EEG) is one of the traditional and easiest tool for the
identification and diagnosis of seizures [5]. The availability of EEG for common
people within their budgetary limits made it a typical method. Due to the sophisticated nature of brain system, the EEG signals acquired from the brain are also
complicated. Automated analysis of EEG signals using modern signal processing
techniques might be effortless and precise for the diagnosis of seizures rather than
manual approach [6].
Out of modern signal processing techniques, empirical mode decomposition
(EMD) is one of the widely used techniques for the efficient interpretation of
signals and images. After the introduction of EMD by Huang [7] in 1998, several
65