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EEG-based Seizure Detection Using Discrete Wavelet Transform through Full-Level Decomposition. Duo Chen and Suiren Wan. School of Biological Science ...
2015

IEEE International Conference on Bioinformatics and Biomedicine (BTBM)

EEG-based Seizure Detection Using Discrete Wavelet Transform through Full-Level Decomposition

Duo Chen and Suiren Wan

Forrest Sheng Bao

School of Biological Science & Medical Engineering

Dept. of Electrical & Computer Engineering

Southeast University

University of Akron

Nanjing, Jiangsu, China

Akron, OH, USA

{230139426, srwan}@seu. edu. cn

fba05@uakron. edu

years. The main advantage of DWT is that the resolution of time

Abstract-Electroencephalogram (EEG) is a gold standard in epilepsy

diagnosis

and

has

been

widely

studied

for

epilepsy­

and frequency in DWT can be adapted to the frequency content of

related signal classification. In the past few years, discrete wavelet

the examined patterns, thus leading to an optimal time-frequency

transform (DWT) has been widely used to analyze epileptic EEG.

resolution in all frequency ranges [12], [13] . This makes DWT

However, there are two practical questions unanswered: 1. what the best mother wavelet for epileptic EEG analysis is; 2. what the

specially suitable for the analysis of non-stationary signal such

optimal level of wavelet decomposition is. The main challenge in

as EEG [4], [14] .

using wavelet transform is selecting the optimal mother wavelet

However, among seizure detection based on DWT, two ques­

for the given task, as difl'erent mother wavelet applied on the

tions which still unclear are: 1. which mother wavelet is the best

same signal may produces different results. Such a problem also

for epileptic EEG analysis; 2. what the optimal level of wavelet

exist in epileptic EEG analysis based on wavelet. Deeper DWT can

decomposition is. In question 1, selecting the optimal mother

yield more detailed depiction of signals but it requires substantially more computational time. In this paper, we study these problems,

wavelet for a given task is the main challenge in using DWT,

using the most common epileptic EEG classification task, seizure

as different mother wavelet applied on to the same signal may

detection, as an example. The results show that all 7 mother wavelets

produces different results. This problem also exists in epileptic

used in this work achieve high seizure detection accuracy at high

EEG analysis based on wavelet [15]. In question 2, more levels

decomposition levels. Also, decomposition level effects the detection

of decomposition provide more detailed depiction to the signals,

accuracy more significantly than mother wavelets. For all wavelets, decomposition beyond level 7 improves accuracy limitedly and even

but increase the computational cost, sometimes exponential (e.g.,

decreases accuracy. We further study the most effective bands and

RBF kernel SVM [16]), on the other hand.

features for seizure detection. An interpretation to our results is

For space sake, we pick seizure detection, the most commonly

that seizure and non-seizure EEGs differ across all conventional

EEG classification problem [4], as an example to study. This

frequency bands of human EEG rhythms. The best accuracy of seizure detection achieved in this research is 92.30% using coif3

paper aims at: 1. finding the best mother wavelet for seizure

from levels 2 to 7.

detection; 2. finding the trade-off between decomposition level and seizure detection accuracy. The machine learning approach

Keywords-Seizure detection, EEG, wavelet, decomposition level

to seizure detection is to classify seizure and non-seizure EEGs I.

INTRODUCTION

recorded simultaneously from multiple channels [3] , [17]. worldwide,

Seven families of 54 total mother wavelets are used in this

epilepsy is the second most COlmnon neurological disorder.

research. For each mother wavelet, we decompose the signal to

Epilepsy is characterized by recurring seizures caused by ab­

the maximum allowed levels, the full-level decomposition. Not

Affecting approximately

60

million

people

normal discharges in the brain [1]. Directly recording the neuro­

only do we study the relationship between decomposition level

electric activities, electroencephalogram (EEG) is a gold standard

and accuracy, we also perform feature selection [3] and wavelet

in epilepsy diagnosis. Diagnostic tasks for epilepsy, such as

band selection. Choosing suitable features that can best represent

seizure detection [2]-[4], spike detection [4] , [5] and focus

the characteristics of the EEG signals is important for EEG

localization [6] , [7] , usually require long-term EEG recording

classification [10] . Features used in this research are those well­

up to a few days. Therefore, many computer-aided solutions

known in wavelet-based EEG signal classification [13] , [18] .

have been developed to assist neurologists. Combining signal

Results show that given deep enough decomposition, all

processing and machine learning, most of those approaches

mother wavelets deliver similar results. Furthermore, consistently

model the problem as classification of signals, such as epileptic

across all mother wavelets, decomposition beyond certain level

vs. healthy for epilepsy diagnosis [8] , [9] , ictal (on seizure) vs.

provides little accuracy improvement and even sometimes de­

interictal for seizure onset detection [10], [11], etc. The most

creases performance instead. Our explanation to the results is

commonly classification problem is seizure detection [4] where

that too many decomposition levels will cause feature vector

patients' seizure and non-seizure EEG need to be identified [12]. Applying Discrete Wavelet Transform (DWT) on epilepsy­

redundancy. Seizure and non-seizure EEG differs across all

related EEG signal classification is gaining ground in recent

accuracy of 92.30% is achieved by RBF-kernel SVM [19] when

978-1-4673-6799-8115/$31.00 ©2015

IEEE

conventional frequency bands of human EEG rhythms. The best

1596

B. Wavelet Families

using coif3 as mother wavelet and its 7 features from levels 2-7.

In this paper, we test 7 most commonly used wavelet fam­ II.

PROBLEM FORMULATION AND DATASET

ilies' performance on epileptic focus localization using EEG

We formulate the problem of seizure as classifying multi­

signal [4], [14]. The 7 wavelet families are: BiorSplines, Coiflets,

channel EEG recordings (seizure and non-seizure). The dataset

Daubechies, DMeyer, Haar, ReverserBior and Symlets. They

used in this work was collected at the Children's Hospital Boston

include 54 family members (mother wavelets) in total as shown

(MIT, in short), consists of EEG recordings from pediatric

in Table I.

subjects with intractable seizures. Subjects were monitored for up

C.

to several days following withdrawal of anti-seizure medication

Decomposition bands In this research, each wavelet will be tested through full-level

in order to characterize their seizures and assess their candidacy

decomposition. The maximum level L of decomposition is jointly

for surgical intervention [20]. Recordings, grouped into 23 cases,

determined by the signal and the mother wavelet to satisfy the

were collected from 22 subjects (5 males, ages 3-22; and

condition:

17 females, ages 1.5-19). The International 10-20 system of

L
-� 9085 � �80 LI X.

Figure 5: Regression Curve of Decomposition Level and Accu­ racy using best member in each wavelet family

combinations of bands and features (e.g., for bior1. 1, j

13,

it has

8191

x

511

=

4185601

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combinations of bands and

scale function

1.5

wavelet function

1.5

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performed. Because of the high time complexity of band and

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feature selections, we only perform them on mother wavelets that exhibit the best performance in their respective families.

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