NEURAL CLASSIFIER FOR DETECTION

AND CLASSIFICATION OF SPIKES AND SHARP WAVES

Geovani Rodrigo Scolaro, Fernando Mendes de Azevedo and Christine Fredel Boos

Biomedical Engineering Institute, University Federal of Santa Catarina, Florianópolis, SC, Brazil

Keywords: Spikes, Sharp waves, Wavelet, Artificial neural networks, Neural classifier.

Abstract: In this article is discussed the application of a hybrid approach that uses the Wavelet Transform and

Artificial Neural Networks in detection and recognition of epileptiform events in EEG signals. It is

presented the methodology used to develop a Neural Classifier as well as the experiments and its results

through the Neural Networks and Wavelet Transform. The developed Neural Classifier showed good results

in the classification of Epileptiform events with and without pre-processing achieving sensitive of 97.14%,

specificity of 94.55% and accuracy of 96.14%, suggesting the high sample rate of the EEG signals

contributed to achieve these values.

1 INTRODUCTION

Epilepsy is a chronic condition or a group of

diseases with high prevalence, however, still poorly

explained by science. Epileptic seizures are crises

that reflect a temporary dysfunction of a small part

of the brain (focal seizures) or a more extensive area

involving the two hemispheres (generalized

seizures).

The electrographical elements frequently found

in EEG records of epileptic patients are the Spikes

(20-70 ms) and Sharp Waves (70-200 ms). These

events are significative for the epilepsy diagnosis,

which are known as Epileptiform events (Argoud et

al., 2006), (Sörnmo and Laguna, 2005), (Pillai and

Sperling, 2006). In neurological practice spikes

found in the records of electroencephalography

(EEG) are used to confirm a diagnosis of epilepsy

(Pillai and Sperling, 2006) and help identify the type

of syndrome that affects the patient (Niedermeyer

and Silva, 2004).

Automatic detection of Epileptiform events is an

important aspect of long-term epilepsy monitoring

and it is important highlights that the visual analysis

is a slow and exhaustive process being extensively

used as support for the diagnosis of epilepsy (Pillai

and Sperling, 2006). Experts verify screens of signal

composed by 24 or 32 channels in continuous EEG

records with durations up to 15s (Argoud et al.,

2006).

Wilson and Emerson (2002) conducted a study

where algorithms to detect Epileptiform events are

compared. It can be observed that few systems have

practical application, because many of these don’t

prove to have reached an acceptable rate of false

positives per minute (fp/min), resulting in little or

none effective saving of time.

This work contributed to the automating process

of the epilepsy diagnosis, checking the feasibility of

using the Wavelet Transform as a way to processing

the EEG signals as well as the capability of Neural

Network works as a Neural Classifier in the

classification of Epileptiform and Non-Epileptiform

events. It was used this sampling rate of 512 Hz in

attempt to obtain better results in the process of

classification of events in relation to other studies

using lower sampling rate between 100 and 256 Hz

(Argoud et al., 2006), (Sovierzoski, 2008), (Khan

and Gotman, 2003), (Pillai and Sperling, 2006)

(Indiradevi et al., 2008), (Adeli et al., 2002) (Pang et

al. 2003), (Xu et al. 2007).

The results were evaluated using performance

indicators applied to the diagnostic tests. The

algorithms were implemented in C++ Builder 6.

2 MATERIAL AND METHODS

2.1 Bank of EEG Signals

The bank of EEG signals is composed by records of

11 patients truly epileptic. The used signals present

504

Mendes de Azevedo F., Scolaro G. and Fredel Boos C..

NEURAL CLASSIFIER FOR DETECTION AND CLASSIFICATION OF SPIKES AND SHARP WAVES .

DOI: 10.5220/0003170405040509

In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS-2011), pages 504-509

ISBN: 978-989-8425-35-5

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

the following settings: referential montage, 32

channels, 512 Hz of sample rate, band limited 0.5-

40 Hz and notch filter of 60 Hz to eliminate

interference caused by the power line. They were

selected 685 events between spikes, sharp waves,

blinking, background activity and noise.

2.2 Wavelet Multiresolution Analysis

The analysis in time-frequency domain by Wavelet

Transform is performed by taking a Wavelet

prototype function called mother-wavelet. This

mother-wavelet suffers escalations and translations,

forming the daughter-wavelets (1) (Mallat, 1999).





















(1)

where ψ(t) is the mother-wavelet and ψ

a,b

is the

daughter-wavelet, a

-1/2

is the constant of energy

normalization, b is the translation factor and a is the

dilation factor.

The Continuous Wavelet Transform uses

parameters of time and scales continuous. Using

discrete parameters to a and b (a≥1, b≥1) determines

the Discrete Wavelet Transform (2).

 

akbt

baDWT

























(2)

where k and i are integers, b

and a

are the

parameters of translation and dilation, respectively.

The Wavelet Multiresolution Analysis is based in

the computational implementation of the Discrete

Wavelet Transform. The algorithm decomposes a

discrete signal using filter banks (Argoud et al.,

2006), (Mallat, 1999), (Indiradevi et al., 2008).

The set of filters H[n] extract the average

characteristics, defined as approximations of the

signal x and added to a set of filters G[n] extract the

features of high-frequency defined as details of the

signal x (Figure 1).

The idea to use the Wavelet Transform is extract

the signal features that somehow can be used as way

of separation between the classes of events before a

selected epoch of signal be analyzed by the neural

classifier.

In this work it was used Wavelet Function

Coiflet1 because this function showed better results

at the detection of Epileptiform Events as seen in

studies conducted by Argoud et al. (1999), Argoud

et al. (2006) and only the details of the

decomposition levels were used to process the

signals.

Figure 1: Representation of the Wavelet Multiresolution

analysis.

2.3 Neural Networks

A Neural Network Multilayer Perceptron has

multiple layers of neurons fully connected (Eberhart

and Dobbins, 1990). The first layer or input layer

receive the patterns, intermediate layers perform the

processing and feature extraction and the output

layer presents the final result. In the last layer the

neurons can have an output function in order to

discretize the results transforming the Neural

Network in a classifier (Haykin, 2001), (Eberhart

and Dobbins, 1990).

If there is only one output neuron the network

becomes a binary neural classifier, as implemented

in this work.

Some events were selected to generated three

sets of patterns: training, validation and test showed

in the Table 1.

Table 1: Description of the pattern sets.

Pattern Set

Spikes

Sharp

Waves

Blinking

Normal

Activity

Noise

Total

Events

Training 29 71 31 40 29

200

Validation 13 87 41 49 10

200

Test 28 148 36 56 17

285

Total

70 306 108 145 56

685

2.4 Indexes of Sensitivity

and Specificity

In the evaluation of the neural classifier the result of

classification and appointment of the expert are

compared. This comparison is also known as

NEURAL CLASSIFIER FOR DETECTION AND CLASSIFICATION OF SPIKES AND SHARP WAVES

505

diagnostic test widely used in medical sciences. The

indicators True Positive (TP) and True Negative

(TN) represent the agreement in the classification of

the correct decisions. The False Positive (FP) and

False Negative (FN) rates represent the

disagreement in the classification (Jekel, 2001).

Totaling the indicators described above can be

calculated the sensitivity, specificity and accuracy.

Sensitivity (3) is the ability of the classifier to

identify the positive events among the truly positive.

ysensibilit





(3)

Specificity (4) is the ability of the classifier to

identify the negative between the truly negative and

both indexes are used in the ROC Analysis.

yspecificit





(4)

Accuracy (5) is the global concordance of the true

positive and negative results in subjects with and

without the sickness (Jekel, 2001).

TNTP

accuracy







(5)

2.5 ROC Analysis

The use of ROC curve (Figure 2) as a performance

measure for classifier systems and diagnostic

systems regardless of their application and it is

employed in expert systems and Artificial Neural

Networks. To measure the performance of the ROC

curve is used the AUC (Area Under the ROC Curve)

index, which present values between 0.5 (no

discrimination) and 1.0 (total discrimination)

(Sovierzoski, 2008), (Jekell, 2001), (Braga, 2000).

Figure 2: ROC Curves.

3 METHODOLOGY

From each EEG channel the selected epochs of

signal has 512 samples. This epoch is submitted to

the Coiflet1 function where is decomposed in 10

detail levels. Each decomposed level presents a

particular signal with some extracted features about

the selected epoch, which highlight the high

frequencies of the signal. These decomposed signals

are compared with a threshold composed by the

mean in addition with the standard deviation of the

EEG signal. If the decomposed signal exceeds the

threshold the analyzed epoch is characterized as an

Epileptiform Event and then presented to the Neural

Network inputs. The neural classifier will classify

this epoch as Epileptiform Event or not. All this

process is showed in the Figure 3.

Figure 3: Overview of the work.

Some experiments were performed extracting

features of the EEG signals with the objective to

identify the better decomposition level to implement

the neural classifier. It was plotted one chart for each

detail level of decomposition with the dispersion of

all the 685 selected events. In the process of

decomposition each detail level presents a signal

with positive and negative amplitudes (Figure 3),

which characterize the high frequencies of the

original signal. It was calculated the absolute value

of the decomposed signal for each detail level to

represent the high frequencies only in one domain.

The absolute values were plotted in the charts. It

was verified that the 5

and 6

levels of

decomposition (Figure 4) were the levels that more

highlighted Spikes and Sharp Waves. Other events

are also highlighted with lower amplitudes. Some of

them showed amplitudes next to the amplitudes of

the Spikes and Sharp Waves.

BIOSIGNALS 2011 - International Conference on Bio-inspired Systems and Signal Processing

506

Figure 4: Groups of Spikes and Sharp Waves at the 5

and

levels of decomposition.

For this reason other experiments were

performed only using the Neural Networks just to

check its performance with signals without

processing.

The topology used to implement the Neural

Network was a Feedforward three-layer, with 512

neurons in the input layer, 10 neurons in the inner

layer and 1 neuron to the output layer, and for all

neurons was used the logistic activation function.

The convention used was the high output (1)

represents the Epileptiform events and the low

output (0) represents the Non-Epileptiform events.

In supervised training procedure of Neural Network

was used the Backpropagation.

For the training it was used the following

settings: random initialization of synaptic weights

with values between ± 0.01, learning rate of 0.002

and momentum of 0.7. The evaluation of the Neural

Network training was performed using the method

of Cross-validation with Early Stopping. In this

method the evaluation of the training and validation

errors are calculated when all the patterns of the

training set and validation set are presented to the

network. The mean square error of training is

calculated from the equation (6).







nynd

))()((

)(



(6)

Similarly, the validation error of the network is

calculated by (7).







nynd

))()((

)(



(7)

In the process of performance evaluation of the

neural classifier was used the AUC index. The

evaluation of the classifier starts when the set of

validation patterns is presented to the network,

where the indicators (TP, TN, FP, FN) were

totalized. From these indicators the sensitivity and

specificity curves are calculated as well as the ROC

curve for each epoch of training allowing identifying

epochs that presented the highest AUC.

4 RESULTS

4.1 Wavelet Transform

It was verified that the 5

and 6

levels of

decomposition (

Figure 4) were the levels that more

highlighted Spikes and Sharp Waves. Other events

are also highlighted with lower amplitudes. Some of

them showed amplitudes next to the amplitudes of

the Spikes and Sharp Waves. In a practical

application would not be possible to define a

threshold decision to perform the separation of these

events using only the amplitudes of the decomposed

signals.

4.2 Neural Classifier

During the training process (Figure 5) can be

observed that the mean square error of the training

curve showed a continuous decay, indicating the

training convergence. The validation curve shows a

decay of the mean square error up to the epoch 530,

reaching the minimum value (MSE

Vmín

=0.05180),

characterizing the early stopping. From the epoch

531 there was an increasing in the error, indicating

the specialization of the training.

Figure 5: Curves of the mean square error of training and

validation for different epochs of training.

NEURAL CLASSIFIER FOR DETECTION AND CLASSIFICATION OF SPIKES AND SHARP WAVES

507

Table 2 shows the performance indexes obtained

with the neural classifier and the Figure 6 shows the

ROC curves of different epochs of training.

The epoch 513 had the highest value for

sensitivity and specificity, therefore, also showed the

highest rates of performance demonstrating that the

best results are obtained next to the epoch 530,

which was the occurrence point of early stopping.

Table 2: Obtained results between the epochs of training.

Epoch MSE

train

MSE

vald

Sens.

[%]

Spec.

[%]

Acc.

[%]

Threshold

AUC

Máx.

1 0,24992 0,24581 81,08 45,02 54,39 0,54 0,62150

50 0,08106 0,09933 91,76 82,61 88,07 0,56 0,99500

312 0,02028 0,05528 96,02 93,58 95,09 0,34 0,99790

513 0,00973 0,05180 97,14 94,55 96,14 0,38 0,99910

530 0,00918 0,05180 97,14 94,55 96,14 0,38 0,99850

1730 0,00123 0,05324 96,05 94,44 95,44 0,24 0,99630

After the evaluation of the neural classifier a

final test was performed by selecting the epoch 513,

which represents the highest AUC (AUC

Máx

0.99910).

Figure 6: ROC curves for some epochs of training. It can

be observed the epoch 513 had the higher AUC index.

Figure 7: Representation of the classification performed

with the test pattern set selecting the epoch 513 (Highest

AUC Index).

It was used a set of test with 285 events. Figure 7

shows the classification made by the neural

classifier, based in the epoch 513 of training

reaching values of sensitivity of 97.14%, specificity

of 94.55% and accuracy of 96.14%

5 CONCLUSIONS

In the obtained results using the Wavelet Transform

was observed that only the amplitude of decomposed

signals cannot separate Epileptiform and Non-

Epileptiform events reaching values of sensitivity of

96.43%, specificity of 88.03% and accuracy of

92.98%. Further studies are being made with the

Wavelet Multiresolution Analysis to signals with

512 Hz of sample rate.

The neural classifier evaluation was performed

using the performance indexes (AUC index and

accuracy index). These indexes could be an efficient

way to verify the performance of the classifier. The

best results of the classifier training were at the

epochs that the indexes obtained are located near to

the epoch indicated by the early stopping. The

experiments with the neural classifier using signals

without processing reached better results than

signals processed by the Wavelet Transform:

sensitivity of 97.14%, specificity of 94.55% and

accuracy of 96.14%.

It can be concluded that the high sample rate of

the EEG signals influence directly in the recognition

process. With a high sample rate more pattern details

are passed to the Neural Network inputs, improving

distinction between the events, which allowed

achieve better results without the need to pre-process

the EEG signals. However, the high sample rate

means more details about the signal, and fast

variations present in the signal that characterized

high frequency are highlighted too. This implied in

an increase of false positives due to the fact that the

Wavelet Transform confuse fast variations with

spikes. This fact explains the difference between the

rates of sensitivity and specificity among the use or

not of the Wavelet Transform as pre-processing the

inputs of neural classifier.

REFERENCES

Wilson, S. B., Emerson R., 2002. Spike Detection: a

review and comparison of algorithms. Clinical

Neurophysiology. Vol. 113, pp. 1873-1881.

Niedermeyer, E., Silva, F. L., 2004.

Electroencephalography: Basic Principles, Clinical

BIOSIGNALS 2011 - International Conference on Bio-inspired Systems and Signal Processing

508

Applications, and Related Fields. Lippincott Williams

e Wilkins.

Sörnmo, L., Laguna, P., 2005. Bioelectrical Signal

Processing in Cardiac and Neurological Applications.

Elsevier Academic Press, USA.

Pillai, J., Sperling, M.R., 2006. Interictal EEG and the

Diagnosis of Epilepsy. Epilepsia. v. 47 (Suppl. 1): 14-

22, Blackwell Pub. ILAE.

Haykin, S., 2001. Redes Neurais: Princípios e Prática.

Bookman.

Mallat, S., 1999. A Wavelet Tour of Signal Processing.

Academic Press, USA.

Eberhart, R. C., Dobbins, R.W., 1990. Neural Networks

PC Tools. A Practical Guide. Academic Press.

Jekel, J. F., Katz, D. L., Elmore, Joann G., 2001.

Epidemiology, Biostatistics and Preventive Medicine.

edition, W. B. Saunders Company p.108-109.

Argoud, F. I. M., De Azevedo, F. M., Marino Neto, J.,

Bittencourt, P. C. T., 1999. Development of an

automatic system of detection and classification of

epileptiform events in EEG. Medical & Biological

Engineering & Computing. v.37, p.490 – 491.

Argoud, F. I. M., Azevedo, F. M., Marino Neto, J. and

Grillo, E., 2006. An effective system for automated

detection of epileptiform events in long-term EEG

based on context information. Medical and Biological

Engineering and Computing. SpringerLink.

Sovierzoski, M. A., 2008. Evaluation of ANN Classifiers

During Supervised Training with ROC Analysis and

Cross Validation. International Conference on

Biomedical Engineering and Informatics. IEEE

Computer Society.

Khan, Y. U., Gotman, J., 2003. Wavelet based automatic

seizure detection in intracerebral

electroencephalogram. Clinical Neurophysiology.

Elsevier.

Braga, A. C. S., 2000. Curvas ROC: Aspectos Funcionais

e Aplicações. Doctorate Thesis, University of Minho,

Braga, Portugal.

Indiradevi, K. P., Elias, E., Sathidevi, P. S., Nayak, S. D.,

Radhakrishnan, K., 2008. A multi-level wavelet

approach for automatic detection of epileptic spikes in

the electroencephalogram. Computers in Biology and

Medicine. Elsevier.

Adeli, H. Zhou, Z. Dadmehr, N., 2002. Analysis of EEG

records in an epileptic patient using wavelet transform.

Journal of Neuroscience Methods. Elsevier.

Pang, C. C. C., Upton, A. R. M., Shine, G., Kamath, M.

V., 2003. A Comparison of Algorithms for Detection

of Spikes in the Electroencephalogram. IEEE

Transactions on Biomedical Engineering. Vol. 50, No.

4, April.

Xu, G., Wan, J., Zhan, Q., Zhan, S., Zhu, J., 2007. A spike

detection method in EEG based on improved

morphological filter. Computers in Biology and

Medicine. Elsevier.

NEURAL CLASSIFIER FOR DETECTION AND CLASSIFICATION OF SPIKES AND SHARP WAVES

509