Classification of Seizure and Seizure-free EEG Signals ... - IEEE Xplore

Proceedings of the 19th International Conference on Digital Signal Processing

20-23 August 2014

Classification of Seizure and Seizure-free EEG Signals using Multi-Level Local Patterns T.Suneel Kumar

Vivek Kanhangad

Ram Bilas Pachori

Electrical Engineering, Indian Institute of Technology Indore, India [email protected]



Time-domain approaches for detection of epileptic seizures in EEG signals include approaches such as the linear prediction (LP) [5], fractional linear prediction [6] and principal component analysis (PCA) based schemes [7]. In addition, artificial neural network (ANN) based epileptic detection method proposed in [8] also uses time-domain features. Approaches such as the one introduced in [9], which utilizes features derived from Fourier transform of EEG signals for classification of epileptic fall under the category of frequency-domain based methods.

Abstract—This paper introduces a new discriminant feature Multi-level local patterns (MLP) for classification of seizure and seizure-free electroencephalogram (EEG) signals. The proposed approach employs Empirical mode decomposition (EMD) in order to decompose non-stationary EEG signals into intrinsic mode functions (IMFs). Multi-level local patterns are computed for each of these IMFs by performing comparisons in the local neighborhood of a sample value of the signal. Finally, a feature set is formed by computation of histograms of MLPs. In order to classify the EEG signal based on these features, we employ the nearest neighbor (NN) classifier, which utilizes scores computed from matching of histogram features of MLPs to determine the category of the EEG signal. Experimental evaluation of this approach on publicly available EEG dataset yielded improved classification accuracies as compared to the existing approaches in the literature. The best average classification accuracy of the proposed approach is 98.67%, which demonstrates the discriminatory capability of the proposed multi-level local patterns.

Several approaches that fall under the time-frequency category have also been proposed in the literature. These approaches consider EEG signals as having non-stationary characteristics.This category includes time-frequency distribution [10], wavelet transforms [11-15] and empirical mode decomposition based techniques [16]. The key contribution of this work is the introduction of a novel discriminant feature for classification of seizure and seizure-free EEG signals. The proposed descriptor is primarily based on the local binary patterns (LBP) [17], which has been quite effective for various image analysis tasks. Authors in [18, 19] investigated the effectiveness of one-dimensional (ID) version of LBP for speech recognition and onset detection of myo-electric signals. In this work, histogram features of multilevel local patterns are proposed for classification of seizure and seizure-free EEG signals. The motivation behind exploring multi-level local patterns is that the sample values of EEG signal can take on positive as well as negative real values. However, LBP, which was originally proposed for images, basically performs the comparison in the local neighborhood of a pixel and encodes the information in only two states (0 or 1). The above drawback of the LBP has led us to explore multi-level local patterns, which takes into consideration both positive and negative values of samples of the signal and encodes the information in multiple states.

Keywords— Multilevel local pattern (MLP), Local binary pattern (LBP), Empirical mode decomposition (EMD), Intrinsic mode function (IMF), electroencephalogram (EEG) signals, Seizure and Seizure-free EEG signals.

I.

INTRODUCTION

Human experts primarily rely on Electroencephalogram (EEG) signals for diagnosis of neurological disorders such as epileptic seizures. Epilepsy has emerged as a major neurological disorder affecting nearly 1% of the world’s population [1]. The onset of epileptic seizures causes changes in EEG signal characteristics, which are mainly characterized by frequently occurring spikes [2]. The automatic seizure detection based on EEG signal processing has been found to be very useful for diagnosis of epilepsy [3]. Of late, automated classification of epileptic seizures has drawn a lot attention of researchers and as a result, several methodologies have been proposed in the literature. Research work in this area can be broadly categorized into four categories namely time-domain, frequency-domain, timefrequency and non-linear methods of analysis, based on the signal processing methods used for extraction of discriminatory features [4].

The rest of the paper is organized as follows: section II. details the proposed scheme. This section provides the block diagram of proposed approach, and brief description of empirical mode decomposition technique. Later in this section, we introduce the proposed multi-level patterns and provide descriptions of information fusion and classification schemes

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EMD

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IMF 1

Segmentation & MLP

Histogram of MLP & Concatenation

IMF 2

Segmentation & MLP


IMF 3

Segmentation & MLP


EEG Signal Acquisition

Feature Matching

Fusion of Scores

Nearest Neighbor Classifier

Features from Training data

Seizure

Seizure-free

Figure 1.Block diagram of the proposed approach when first three IMFs are considered for classification employed in this work. Section III briefly describes dataset and presents results from experimental evaluation of the proposed approach. Concluding remarks are provided in section IV. II.

B. Segmentation and Multi-level local pattern IMFs obtained in the previous section are further processed to extract discriminatory information that is useful for classification of EEG signals. Each of the IMFs is divided into smaller segments of equal length. This is followed by computation of MLPs for each of these segments. Mathematically, computation of MLPs can be expressed as follows:

METHODOLOGY

The block diagram of the proposed approach is shown in figure 1. In the first step, the approach involves decomposition of EEG signals into IMFs using Empirical mode decomposition (EMD). Each of the IMFs thus generated is divided into smaller segments. This is followed by computation of multi-level local patterns and their histograms for each of the individual segments. In order to obtain the final representation, histograms of MLPs of individual segments are concatenated. During classification, the matching scores generated by matching corresponding histogram features of the query and training dataset are combined to obtain a consolidated score. The NN classifier utilizes this score to determine the category of the query EEG signal. The proposed approach is detailed in the following sections.

MLPLHS { x[ n ]} = MLPRHS { x[ n ]} =

∑ S { x [ n + k − L ] − x [ n ]} 4 2 L −1− k

(2)

∑ S { x [ n + k + 1 − L ] − x [ n ]} 4 2 L −1− k

(3)

k =0 2 L −1 k=L

MLP{ x[ n ]} = MLPLHS { x[ n ]} + MLPRHS { x[ n ]}

(4)

Here x[n] represents the current sample of the EEG signal, L is the number of comparisons performed on either side of the current sample and the function S with sample values x[n] and x[n1 ] as input parameters, is defined as follows,

A. Empirical mode decomposition In the proposed approach for classification of seizure and seizure-free EEG signals, the EEG signals are first decomposed into intrinsic mode functions (IMFs) using empirical mode decomposition (EMD) [20]. EMD decomposes non-stationary signals into a finite number of modes known as IMFs. Each IMF consists of a mono frequency or narrow band of frequency. These IMFs are sorted in decreasing order of frequencies, i.e., first IMF contains the highest frequency components and last IMF contains lowest frequency components. A signal x(t ) can be represented after decomposition as follows:

x(t ) = ∑ ci ( t ) + r (t )

L −1

when x[n] >0: ⎧ ⎪ ⎪ S { x[n1 ], x[n]} = ⎨ ⎪ ⎪⎩

0

if

x[ n1 ] < − x[ n]

1 2

if if

− x[ n] < x[ n1 ] ≤ 0 0 < x[ n1 ] ≤ x[ n]

3

if

x[ n] < x[ n1 ]

when x[n] 0

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a) Euclidean distance, which is a dissimilarity measure, is computed as follows. Mathematical expression for computation of Euclidean distance is

where x[n1 ] is one of the neighboring samples, with which the current sample x[n] is being compared. It may be noted from (4) that the core of the proposed multi-level local pattern is to capture local variations in the signal more effectively by performing comparisons in the local neighborhood of a sample and encoding the information thus generated. Finally, histograms of MLP values are computed for individual segments and are concatenated to form a feature set for the corresponding IMF. Therefore, the above process yields three histogram features when first three IMFs are considered for classification.

E (Y , Z ) =

(5)

i =1

b) Histogram intersection on the other hand is a similarity measure and is computed using the following equation L

H (Y , Z ) = ∑ min (Yi , Z i )

(6)

i =1

Pseudo code of our approach for computation of multi-level local pattern based features is given as follows:

The above described process generates three matching scores (corresponding to three histogram comparisons), when first three IMFs are considered for classification.

function multilevellocalpatterns( X , L) Inputs : X , L, (required : mi n imum lenghth of X > 2 L) 2 L → Length of Multi Scale Local Pattern Output : Histogram of Local Patterns for n = L : length( X ) - L do for j = 0 : 2 L do if ( j ≠ L) do Case 1 if X [n] > 0 S{ j} = 0 if S{ j} = 1 if S{ j} = 2 if S{ j} = 3 if Case 2 if X [n] < 0 S{ j} = 0 if S{ j} = 1 if

L

∑ (Yi2 − Zi2 )

X [n + j - L ]