Ontheclassificationof ee gsignalbyusingansvmbasedalgorythm

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On the Classiﬁcation of EEG Signal by Using an SVM Based Algorithm
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Valeria Saccà
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Maurizio Campolo
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Domenico Mirarchi
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Antonio Gambardella
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On the classification of EEG signal by using an
SVM based algorythm
Valeria Sacca´, Maurizio Campolo, Domenico Mirarchi, Antonio Gambardella,
Pierangelo Veltri, and Carlo Francesco Morabito
University Magna Graecia Catanzaro, Italy
University Mediterannea Reggio Calabria, Italy
Abstract. In clinical practice, study of brain functions is fundamental
to notice several diseases potentially dangerous for the health of the sub-
ject. For this goal, Electroencephalography (EEG) can be used to detect
cerebral disorders but EEG study is often difficult to implement, espe-
cially for the signal dimensions and noise presence. For this reason, there
is the necessity to have efficient and accurate methods to overcome these
obstacles. In the field of Signal Processing, there exist many algorithms
and methods to analyze and classify signals that can be used to support
physicians while reducing and extracting useful information from EEG.
Support Vector Machine (SVM) based algorythms can be used as classifi-
cation tool in several field and can be applied for classifying EEG records.
SVM based tools allow to obtain an efficient discrimination between dif-
ferent pathology and to support physicians while studying patients.
In this paper, we report an experience on designing and using an SVM
based algorythm to study and classify EEG signals. We focus on Creutzfeldt-
Jakob disease (CJD) EEG signals by means of studying signal properties.
To reduce the dimension of the dataset, principal component analysis
(PCA) are used. These vectors are used as inputs for the SVM classifier
with two classification classes: pathologic or healthy. The classification
accuracy reaches 96,67% and a validation test has been performed, using
unclassified EEG data.
Key words: Classification; SVM; Early Detection.
1 Introduction
The EEG is a non-invasive analysis used to study brain activity by recording
cerebral waves placing electrodes along the scalp. Each scalp area produces waves
that allow to reflect the state of cerebral health. In case of diseases, EEG anal-
ysis shows several abnormalities in recorded signal waves. The identification of
these abnormalities enables the physician to estimate the disease and its stage,
in order to simplify the clinical diagnosis. This process can be also used in the
biomedical researches to investigate cerebral disease characteristics. One of the
main difficulties in studying EEG signals is represented by signal dimensions
and noise presence, thus that it is often difficult to implement an automatic

2 Sacca’, Campolo, Mirarchi, Gambardella, Veltri, Morabito
abnormality detection [1]. This represents a huge disadvantage because the lack
signal understanding could bring an incorrect diagnosis, causing inconvenience
for patients (both in case of false or positive diagnosis). For this reason, there is
the necessity to have efficient and accurate methods to support signals reading,
preprocessing and storing, allowing appropriate diagnosis and therapy designing.
Nevertheless, due to the high number of features automatic data reduction and
classification of EEG signals is often mandatory to support physicians in diag-
nosis definition. Accordingly signal processing and pattern recognition methods
are available and can be used to support EEG signal analysis.
In the last few years, considerable results have been produced according to
high performing hardware and innovative algorithms allowing the analysis of
EEG signals and the extraction of useful information for brain studies [2]. In
literature there are many classification algorithms used to analyze EEG signals.
The method consists to group signals in classes and to identify differences be-
tween them. By defyning classes, a model can be designed to assign data samples
(i.e., signals or portion of them) into classes. Models can be used then to imple-
ment algorithms (i.e., classifier) to classify input signals into known classes (i.e.,
healthy or non healthy classes) [4] [5] [6] [7] [8] [9].
Artificial Neural Network (ANN) and Support Vector Machine (SVM) based
algorithms are examples of classifiers that can be used to study EEG signals.
E.g., [3] [4] [5] [6] [7] [8] [9] [10] [11] and [12] [13] [14] are examples of brain sig-
nals analysis performed by using classifiers. An advantage of using classification
algorithms is an improvement into error reduction in diagnosis definition, thus
supporting physicians in large scale data analysis. Many examples EEG classi-
fiers are available, each specialized in particular diseases (e.g., epilepsy [3] [4] [5]
[6] [7], brain tumors [8], schizophrenia [9]).
We experienced in analysing and classifying EEG signals in particular to
investigate Alzheimer’s disease and epilepsy [15] [16]. Also onset signals have
been detected in [17] for ECG signals.
In this paper, we report on using SVM based classifier to analyze EEG sig-
nals. The aim is to define an algorithm able to: (i) define a classification tool
trained by means of pathological and healthy patients and (ii) perform test by
using blindly healthy and non healthy signals. We used EEG signals related to
CreutzfeldtJakob disease (CJD) healthy patients. Data signals derived by real
patients conditions and have been extracted from available clinical dataset. We
focus on CJD disease as a rapidly progressive one, characterized by the accu-
mulation of an abnormal protein in the brain [18]. Early and reliable diagnosis
of CJD is crucial to avoid progressive encephalopathies, which could be fatal for
the patients. Since early diagnosis of CJD is complicated by the marked hetero-
geneity of clinical presentation of the disease, the contribution of this work is
to define a classifier that can be used as decision support system for clinicians.
EEG signals exhibit several characteristic in considered pathological condition,
depending on the stage of the disease. The here classification algorithm is thus
based on the identification of the periodic sharp wave complexes (PSWC) that
represent the hallmark EEG finding in patients with CJD. We designed and im-

On the classification of EEG signal by using an SVM based algorythm 3
plemented a classifier model of EEG signals, considering the CreutzfeldtJakob
disease (CJD) based on SVM as classification algorithm. The here proposed
method has been testing on clinical data provided by University Magna Graecia
Clinicians group and the obtained results will be used to optimize the process
and for the model tuning.
1.1 Related Works
SVM is a machine learning based method and it has been largely used recent
as kernel for classification tools. E.g., in [19] has been performed a comparison
among SVM and ANN (artificial neural network), to classify eyes blinking show-
ing better accuracy of the SVM based tool. Recently SVM algorithm has mainly
used in EEG classification to study several brain diseases, as for instance in [3]
[4] [5] [6] [7] [8] [9]. In [3] an SVM classifier for EEG signals is presented and used
to detect the onset of epileptic seizures. Features are generated for both seizure
and not seizure activity and a RBF kernel has been chosen, with optimal results.
Moreover the EEG classification can be used to investigate the brain tumors,
as in [8], and to detect drowsiness onset while driving [10]. In the cited papers,
a spectral analysis method has been applied for extracting generic features by
the signals. Nevertheless, the spectral analysis, i.e. the FFT method is based on
simple functions (i.e. sinusoids) and is not suitable for complex signals as EEG
ones. We derive features by using temporal frequency analysis, using a continue
wavelet transform, that is closest with non-stationary signals, as the EEG [20],
by using similar approach than the ones reported in [21] [22]. SVM model has
also be used for schizophrenia diseases in [9], where features extraction is per-
formed by using an autoregressive model (AR) to preprocess data and SVM is
then used to classify signals. To reduce features (data preprocessing) [9] used
linear discriminant analysis (LDA). We used PCA for this purpose that has a
more effective reduction skill than LDA [23].
2 Preprocessing and Data Features extraction
The aim of this paper is to define a classifier to support decision in diagnosis an-
alyzing EEG signals. Data set needs to be preprocessed and normalized to train
SVM based classifier methods. We report about used data set and preprocessing
methods. Sixty EEG signals have been crawled from clinical database referring
to several patients. In particular, thirty signals relate to healthy subjects and
thirty ones refer to CJD patients. These signals have been processed by using the
following workflow: (i) preprocessing phase, aiming to reduce artifacts and noise;
(ii) features extraction phase, by using a wavelet transformation; (iii) principal
component analysis (PCA) and normalization phase, aiming to remove the re-
dundant data; (iv) SVM phase, to generate the classifier model. The input data
set generated for the SVM is represented by an NxM matrix where the rows
represent the EEG signals and the columns represent the extracted features. We
now report on how to extract features and thus how matrix has been created.

Fig. 1. Example of an EEG Signal
EEG signals are gathered by using nineteen electrodes placed along the patient
scalp. Each electrode generates a signal containing information and artifacts gen-
erated from sources external (i.e., electrical interference) or internal (e.g., eyes
movement) to patient (see Figure 2). To eliminate artifacts EEG signals must be
preprocessed. In our data set each signal has been cleaned in a semiautomatic
way, by selecting and cutting the artifact of interest. Each signal has been re-
Fig. 2. Example of artifact

Fig. 3. SVM Classifier Block Framework
duced from ten minutes recording to couple of minutes. Physicians support in this
phase is required. We have been supported by physicians of University Magna
Graecia medical school. The preprocessed signals are then manipulated through
a feature extraction process. Extracting the features aims to reduce the data
complexity and to simplify the sequel information process. An analysis in time
domain has been carried out, using the Continuous Wavelet Transform(CWT).
The CWT is an effective tool in signal processing due its attractive properties
such as time/frequency localization (extracting features at various locations in
space at different scales). Using these properties, the desired features can be
extracted from an input signal. In CWT, the signal to be analyzed is matched
and convolved with the wavelet basis function at continuous time and frequency
increments and as result the original signal is expressed as a weighted integral of
the continuous basis wavelet function [20]. In this paper, the considered wavelet
is mexh, which is very close to the type of format wave request. Mean, variance
and skewness features have been evaluated for each signal.
Each signal captured by each electrode has been divided in twenty-four
epochs of five seconds and the CWT has been applied to evaluate the afore-
mentioned features for each signal. Each signal has been divided in three bands,
thus calculating three values for mean, variance and skewness. Finally, the mean
of these three values has been evaluated for each signal, i.e., obtaining for each
signal respectively 4 values for the mean, 4 values for variance and 4 values
for skewness, obtaining twelve features for each EEG signal. Since each EEG
is generated by using 19 electrodes, the final number of features extracted for
each subject is equal to 228 features. In the following the pseudo code used for
features extraction is reported.
For the experienced dataset, we used 60 subjects, thus that the matrix gen-
erated by the preprocessing and features extraction phases is an NxM matrix
where N is 60 and M is equal to 228. We consider an M+1 column value, where
we distinguish healthy from non healthy patients, assigning 1 for the patholog-

ical patients and the value 0 for the healthy patients. We thus obtain a matrix
M(60x229). Latter column in the right part of Table 1 is used to distinguish
healthy from non healthy patients.
Patient Healthy Patient Healthy Patient Healthy Patient Healthy Patient Healthy Patient Healthy
Id Status Id Status Id Status Id Status Id Status Id Status
1 1 11 1 21 1 31 0 41 0 51 0
2 1 12 1 22 1 32 0 42 0 52 0
3 1 13 1 23 1 33 0 43 0 53 0
4 1 14 1 24 1 34 0 44 0 54 0
5 1 15 1 25 1 35 0 45 0 55 0
6 1 16 1 26 1 36 0 46 0 56 0
7 1 17 1 27 1 37 0 47 0 57 0
8 1 18 1 28 1 38 0 48 0 58 0
9 1 19 1 29 1 39 0 49 0 59 0
10 1 20 1 30 1 40 0 50 0 60 0
Table 1. The training vector and classes: Patient Id is the subject identifier while
health status may be 0 or 1 (healthy or non healty). The vector has been divided in
groups of ten
To apply an SVM module and thus to obtain a classifier, data needs to be
reduced in size and also needs to be treated in order to obtain more homo-
geneous values. Indeed, the more large is dataset, the more high is the prob-
ability of obtaining errors or misclassification. Similarly, the more values are
non-homogeneous, it is more likely that SVM classifier is not able to discrimi-
nate between the considered classes. To reduce the size of the dataset, a method
of Blind Source Separation (which is the separation of a set of source signals
from a set of mixed signals) has been used, to improve SVM performance (as in
[24] [25]). We used Principal Component Analysis (PCA) to reduce the number
variables (representing features), because it is the most appropriate technique
for our purpose, allowing to transform the original data into a new set of vari-
ables that preserve the information contained in the original data set without
redundancy. To make values more homogeneous a normalization algorithm has
been applied and values have been mapped into a (-1, +1) range. Finally, the
algorithms have been implemented and run by using the Matlab programming
environment and LibSVM [26].
3 SVM based classifier
Data set are used to train and develop the SVM based classifier. To perform the
training set we need to use the class labels, that in our cases are the patients
healthy status, and the features. The training data is used to produce a model,
which is able to predict the target values of the test data [27]. We used the
leave-one-out as training method [28]. It consists in calculating the model with

the exclusion of one object at a time and predicting its value. Starting from
a data set, it works as follows: (i) remove one element from the data set; (ii)
define a prediction model using the data (less the one removed); (iii) predict and
assign the removed element to a class by using the defined model; (iv) repeat
the procedure for all elements. The workflow of the Leave-one-out algorithm is
reported in Figure 4.
The classification module is then implemented by using the code reported
in the following. To complete the algorithm, the implementation requires the
definition of a kernel function. The 4 most used functions are: (i) Linear; (ii)
Polynomial; (iii) Radial Basis Function (RBF); (iv) Sigmoid. We used an RBF
kernel function because it is most appropriate in the biomedical signal processing
and requires the setting of two parameters: boxconstraint or C and γ. γ defines
how far the influence of a single training example reaches, while the boxconstrain
C controls the classification and the misclassification, due to data overfitting. To
choose these values have been made several iteration tests by evaluating the
classification result and finally the values that gave better accuracy was chosen.
In the SVM training phase, a RBF kernel with C and γ amounting to 1 has been
used and the training vector was the last column of dataset. The leave-one-out
pseudo code is reported in the following.
Leave one out algorithm (pseudo code)
1 %leave one out
d = training vector
3 xs = PCA and normalization output dataset
for i = 1 to 60
5 xtrain =[ xs (1:i-1, 1:59); xs(i+1:60 ,1:59)];
dtrain =[ d(1:i -1); d(i+1:60)];
7 Training svm (’kernel_function ’,
’rbf ’,’RBF_sigma ’, 1,
9 ’boxconstrain ’, 1);
end
Results are used to define a confusion matrix, used to compare the predicted
elements with class belonging (real and predicted). By using the confusion ma-
trix, it can be possible to calculate four parameters: true positives (TP), true
negatives (TN), false positives (FP) and false negatives (FN). These values allow
to calculate the accuracy, the specificity and the sensibility.
Accuracy (acc), sensitivity (sens) and specificity (spec)
Evaluate TP , TN , FP and FN by the confusion matrix
2
acc = (TP+TN)/(TP+TN+FP+FN);
4 sens = TP/(TP+FN);
spec = TN/(TN+FP);
The accuracy indicates the closeness between elements assigned to predicted
classes and their belonging. The metric used consists in evaluating the accuracy
as the sum of TP and TN divided by the sum of all found values (TP, TN, FN,
FP). The specificity measures the ability of correctly predict healthy patient (i.e.,

negative) and can be calculated as TN minus (all the) negative results. Finally,
the sensibility is evaluated as TP minus (all) positive values found, and represents
the ability to predict the non healthy subjects on a reference population.
4 Experimental Results
We used the algorithm reported above to define an SVM based classifier. The
used data set consists of 60 available EEG signals. We used part of the data
as training while part of them as tests. The performance of the SVM classifier
can be used to test and produce diagnosis of CJD, considering EEG track with
PSWC as hallmark of disease. Using the 60 signals in the dataset, the accuracy
of our classifier is 96,67%, the specificity is 93,33% and the sensibility is 100%.
In the Table 2, we report the confusion matrix obtained.
To improve the accuracy, sensibility and specificity, we used 9 additional EEG
signals of subjects suffering from CJD. These signals have been preprocessed
and added to the dataset. The SVM classifier has been trained by using different
values of C and γ. By setting C and γ both to 0,4(also in this case we were carried
out iterative test for the choice of the best parameters), and using the whole
set of data, the value of accuracy is 97,10%, the sensitivity 96,67% while the
Fig. 4. Leave one out algorithm workflow

30 0
2 28
Table 2. Confusion matrix results by using the SVM classifier
specificity 97,44%. The addition of these new signals has improved the accuracy
and sensitivity of the classifier, while the specificity is slightly lower. In Table 3,
we report the confusion matrix obtained by using the increased data set.
29 1
1 38
Table 3. Confusion matrix obtained by using 69 EEG signals
5 Conclusion
CJD diagnosis is a difficult task, performed by analyzing EEG signals as well
as clinical background. Nevertheless, EEG signal analysis is a difficult task due
to high dimensional features. CJD early detection is mandatory to reduce risks
or complications for patients. We defined and applied a methodology to define
an SVM based classification tool. First of all EEG signals have been elaborated
by using wavelet based mapping function and by evaluating statistical features.
Principal Component Analysis (PCA) and normalization are used to reduce and
to reduce data heterogeneity. An SVM classifier is then defined. For the training
phase, a leave-one-out based algorithm has been implemented and though the
confusion matrix, we have calculated the accuracy, which had 96,67%. The per-
formance of our SVM classifier confirms the classification ability and a candidate
as decision support system for EEG analysis in case of CJD suspect cases.
Acknowledgments.
The authors would like to thank Rocco Cutellé for his support and experiments in
denoising and preprocessing signals. Also authors thanks here Umberto Aguglia
and Neurological group for supporting us in furnishing supports for EEG signals.
References
1. Bhuvaneswari, P and Kumar, J Satheesh, Support Vector Machine Technique for
EEG Signals, International Journal of Computer Applications (0975 8887) Volume
63 N.13, February 2013

2. Li, Yan and Wen, Peng and others, Classification of EEG signals using sampling
techniques and least square support vector machines, (Rough Sets and Knowledge
Technology), Springer, 375-382 (2009)
3. Shoeb, Ali H and Guttag, John V ,Application of Machine Learning To Epileptic
Seizure Detection (Proceedings of the 27th International Conference on Machine
Learning), 975-982, (2010)
4. Kumari, R and Jose, J Prabin, Seizure detection in EEG using time frequency
analysis and SVM (Emerging Trends in Electrical and Computer Technology (ICE-
TECT), 2011 International Conference on), 626-630
5. Panda, R and Khobragade, PS and Jambhule, PD and Jengthe, SN and Pal, PR
and Gandhi, TK, Classification of EEG signal using wavelet transform and sup-
port vector machine for epileptic seizure diction (Systems in Medicine and Biology
(ICSMB), 2010 International Conference on), 405-408
6. Li, Shufang and Zhou, Weidong and Yuan, Qi and Geng, Shujuan and Cai, Dongmei,
Feature extraction and recognition of ictal EEG using EMD and SVM (Computers
in biology and medicine, Vol.43, N.7), Elsevier, 807-816 (2013)
7. Temko, A and Thomas, E and Marnane, W and Lightbody, G and Boylan, EEG-
based neonatal seizure detection with support vector machines (Clinical Neurophys-
iology, vol.122, N.3), Elsevier, 464-473 (2011)
8. M. Murugesan and R. Sukanesh, Towards Detection of Brain Tumor in Electroen-
cephalogram Signals Using Support Vector Machines (International Journal of Com-
puter Theory and Engineering, Vol.1, N.5), IACSIT Press, 622 (2009)
9. M Sabeti, and R Boostani, and SD Katebi, and GW Price, Selection of relevant
features for EEG signal classification of schizophrenic patients (Biomedical Signal
Processing and Control, Vol.2, N.2), Elsevier, 122-134 (2007)
10. Yeo, Mervyn VM and Li, Xiaoping and Shen, Kaiquan and Wilder-Smith, Einar
PV, Can SVM be used for automatic EEG detection of drowsiness during car driving
(Safety Science, Vol.47, N.1), Elsevier, 115-124 (2009)
11. Shoker Leor and Sanei Saeid and Sumich Alex, Distinguishing between left and
right finger movement from EEG using SVM (Engineering in Medicine and Biology
Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the),
5420-5423 (2006)
12. Hazarika N., Chen J.Z. ; Ah Chung Tsoi ; Sergejew A., Classification of EEG
signals using the wavelet transform (Digital Signal Processing Proceedings, Vol.1,
pag.89-92), IEEE (1997)
13. Abdulhamit Subasi and Ergun Erelebi, Classification of EEG signals using neural
network and logistic regression (Computer Methods and Programs in Biomedicine,
Vol.78, pag.87-99), 20005
14. Abdulhamit Subasi, Ahmet Alkana, Etem Koklukayab, M. Kemal Kiymika,
Wavelet neural network classification of EEG signals by using AR model with MLE
preprocessing (Neural Networks, Vol.18, pag.985-997), Elsevier (2005)
15. Labate D., Palamara, I., Mammone, N., Morabito, G., La Foresta, F., Morabito,
F.C., SVM classification of epileptic EEG recordings through multiscale permuta-
tion entropy (Neural Networks, pag.1-5, IEEE(2013)
16. Morabito F.C., Campolo M., Labate D., Morabito G., Bonanno L., Bramanti A.,
De Salvo S., Marra A., Bramanti P., A Longitudinal EEG Study of Alzheimer’s
Disease Progression Based on A Complex Network Approach, (International Journal
of Neural System, Vol.25, Issue 02), March 2015
17. Patrizia Vizza, Antonio Curcio, Giuseppe Tradigo, Ciro Indolfi,Pierangelo Veltri, A
framework for the atrial fibrillation prediction inelectrophysiological studies (Com-
puter Methods and Programs in Biomedicine, pag.65-76), Elsevier (2015)

18. Wieser, Heinz Gregor and Schindler, Kaspar and Zumsteg, Dominik, EEG
in Creutzfeldt–Jakob disease (Clinical Neurophysiology, vol 117, N.5), Elsevier,
935951,(2006)
19. Singla Rajesh and Chambayil Brijil and Khosla Arun and Santosh, Jayashree,
Comparison of SVM and ANN for classification of eye events in EEG (Journal
of Biomedical Science and Engineering, Vol.4, N.01, pag.62), Scientific Research
Publishing (2011)
20. Hojjat Adeli, Ziqin Zhou and Nahid Dadmehr, Analysis of EEG records in an
epileptic patient using wavelet transform (Journal of Neuroscience Methods 123,
pag.69-87), Elsevier (2003)
21. Inuso G., La Foresta F., Mammone N. ,Morabito F.C., Brain Activity Investigation
by EEG Processing: Wavelet Analysis, Kurtosis and Renyi’s Entropy for Artifact
Detection, (Information Acquisition, pag.195-200), IEEE (2007)
22. Inuso G., La Foresta F., Mammone N. ,Morabito F.C., Wavelet-ICA methodol-
ogy for efficient artifact removal from Electroencephalographic recordings, (Neurale
Networks, pag.1524-1529), IEEE (2007)
23. Aleix M. Martnez and Avinash C. Kak, PCA versus LDA (IEEE Transactions On
Pattern Analysis And Machine Intelligence, Vol. 23, N. 2), 2001
24. Gursoy and Subast, A comparison of PCA, ICA and LDA in EEG signal classifi-
cation using SVM (2008 IEEE 16th Signal Processing, Communication and Appli-
cations Conference)
25. Subasi, Abdulhamit and Gursoy, M Ismail, EEG signal classification using PCA,
ICA, LDA and support vector machines (Expert Systems with Applications, Vol.37,
N.12), Elsevier, 8659-8666 (2010)
26. Chih-Chung Chang, Chih-Jen Lin, LIBSVM: A library for support vector machines,
(ACM Transactions on Intelligent Systems and Technology (TIST), Volume 2 Issue
3, Article No. 27), April 2011
27. Hsu Chih-Wei and Chang Chih-Chung and Lin Chih-Jen and others, A practical
guide to support vector classification, 2003
28. Jason Weston, Leave-One-Out Support Vector Machines (IJCAI, pag.727-733),
1999
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