EEG Discrimination with Artiﬁcial Neural Networks

ergio Daniel Rodrigues

, Jo

ao Paulo Teixeira

and Pedro Miguel Rodrigues

Polytechnic Institute of Braganc¸a, Braganc¸a, Portugal

University of Porto, Porto, Portugal

Keywords:

Electroencephalogram, Alzheimer’s Disease, Artiﬁcial Neural Network.

Abstract:

Neurodegenerative disorders associated with aging as Alzheimer’s disease (

) have been increasing signiﬁ-

cantly in the last decades.

affects the cerebral cortex and causes speciﬁc changes in brain electrical activity.

Therefore, the analysis of signals from the electroencephalogram (

EEG

) may reveal structural and functional

deﬁciencies typically associated with

. This study aimed to develop an Artiﬁcial Neural Network (

ANN

) to

classify

EEG

signals between cognitively normal control subjects and patients with probable

. The results

showed that the EEG can be a very useful tool to obtain an accurate diagnosis of AD. The best results were

performed using the Power Spectral Density (

PSD

) determined by Short Time Fourier Transform (

ST FT

) with

ANN

developed using

Levenberg − Marquardt

training algorithm,

Logarithmic Sigmoid

activation function

and 9 nodes in the hidden layer (correlation coefﬁcient training: 0.99964, test: 0.95758 and validation: 0.9653

and with a total of: 0.99245).

1 INTRODUCTION

Alzheimer’s disease(

) is a progressive degenerative

neurological disorder of brain that leads to irreversible

loss of neurons (Blennow et al., 2006). Lesions usu-

ally start in the hippocampus (which is an important

structure in memory formation) and in the cerebral

cortex (Blennow et al., 2006; Feldman and Woodward,

2005). In AD patients brain can be observed senile

plaques formed by amyloid plaques and neuroﬁbril-

lary tangles. Amyloid plaques are found outside the

neurons, neuroﬁbrillary plaques are found inside the

neurons and result in the death of the cells. Gradually,

the neurons degenerate and a generalized collapse of

brain tissue occurs (Feldman and Woodward, 2005).

The aging process increases the incidence of

which

is typically diagnosed in aging people (Moreira and

Oliveira, 2005).

Researchers estimate that 1 in every 85 people may

have

in the next 35 years and this disease may

affect about 100 million people by 2050 (Stahl, 2008).

It is therefore necessary to reach a correct diagnosis

before signiﬁcant loss of memory appears, be-

cause early diagnosis enables the treatment conditions

(Blennow, 2005). A tool suitable for assisting

diagnosis is the EEG (Hort et al., 2010).

One of the most frequently observed abnormalities

EEG

activity of

patients is an increase in power

at low frequency bands and a decreased power in the

higher frequency bands(Baker et al., 2008; Rodrigues

and Teixeira, 2011). The deceleration of

EEG

signal

usually occurs in intermediate and severe stages of

(Jeong, 2004).

In the present study we intend to develop an Ar-

tiﬁcial Neural Network (

ANN

) to discriminate

EEG

signals between cognitively normal control subjects

(Cs) and AD patients.

2 MATERIALS AND METHODS

2.1 Selection of Patients and

Controls/EEG Recording

We had the collaboration of thirty four subjects (14

Controls and 20 of

EEGs

were recorded from

the international system 10-20 of 19 electrodes. The

frequency sample was 200 Hz. Then,

EEGs

were

organized in 5 s artifact-free epochs (1000 samples)

(Rodrigues and Teixeira, 2011). It should be noted that

all recordings were digitally ﬁltered with a band-pass

ﬁlter between 1 Hz and 40 Hz.

236

Daniel Rodrigues S., Paulo Teixeira J. and Miguel Rodrigues P..

EEG Discrimination with Artiﬁcial Neural Networks.

DOI: 10.5220/0004249702360241

In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS-2013), pages 236-241

ISBN: 978-989-8565-36-5

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

2.2 EEG Signal Processing

Fourier transform (FT ) allows us to represent the sig-

nal in the frequency domain. It is the spectral

EEG

signal processing currently used (Rioul and Vetterli,

1992).

constitutes a disadvantage of not being able

to provide information both in time and frequency of

signal characteristics. Indeed, in signal transformation

from the time domain to the frequency domain there

is a lost of information about the temporal location

(Blennow,2005). Thus, by observing the

of a sig-

nal is impossible to say where a particular event is

located, since what is obtained is only the frequen-

cies that compose the signal throughout the whole

time interval considered. If a signal is stationary this

drawback is irrelevant. However, if a signal contains

many non-stationary or transient characteristics (char-

acteristics which are usually the most important parts

of a

EEG

signal)

can not detect such processes

(Blennow,2005). Seeking a solution to this problem it

is common to use the Short-Time-Fourier-Transform

(

ST FT

maps a signal using a bidimensional

function deﬁned in time and frequency and represents

a form of compromise between representation both in

time and frequency of this signal. Although the

ST FT

provides information on time and frequency, there is a

disadvantage when choosing a particular size for the

window that will go through the signal, because this

window remains the same for all frequencies. It turns

out that many signals have a window of variable length

which allows a more precise location of a particular

event both in time and frequency, because in most

cases it is impossible to determine an optimal window

size that can ﬁnd events with enough resolution at very

different frequencies(Blennow, 2005). So,

ST FT

also

presents limitations. To overcame the problem, we

used the

ST FT

because it uses a constant time win-

dow and therefore we can analyze the

EEG

signal in

short time stationary periods. Thus, we calculated the

power spectral density (

PSD

), functions that had the

force of power variations as a frequency function. The

PSD

(

x,w

[k]

) was calculated by the autocorrelation

function (

(u)

) of

ST FT

of the signal, as can be

seen in the follow equations (Rodrigues and Teixeira,

2011).

[u] =

(

∑

N−m−1

n=0

x[n] · x[n + u] , u ≥ 0

∗

[−u] , u < 0

(1)

(2)

x,w

[k] =

· DFT S



[u]



2N−1

∑

u=0

[u] · e

− j·

2πk

2N−1

·u

where, k = 0, ..., 2N − 1.

The

PSD

was normalized to the scale 0 to 1 and it

was designated

PSD

(Rodrigues and Teixeira, 2011),

as can be observed in the next equation.

PSD

[m, k] =

x,w

[n]

∑

n=k

x,w

[n]

, m = 0, ..., N

− 1 (3)

where k

and k

represent discrete cutoff frequencies.

2.3 Feature Extraction

The relative power (

) in

EEG

conventional frequen-

cies bands was obtained by the sum of the components

of the

PSD

in the conventional frequency bands: delta

(

, 1-4 Hz), theta (

, 4-8 Hz), alpha (

, 8-13Hz), beta1

(

, 13-19 Hz), beta2 (

, 19-30 Hz) and gamma (

30-40 Hz).

RP(δ) =

4Hz

∑

f =1Hz

PSD

( f ) (4)

RP(θ) =

8Hz

∑

f =4Hz

PSD

( f ) (5)

RP(α) =

13Hz

∑

f =8Hz

PSD

( f ) (6)

RP(β

) =

19Hz

∑

f =13Hz

PSD

( f ) (7)

RP(β

) =

30Hz

∑

f =19Hz

PSD

( f ) (8)

RP(γ) =

40Hz

∑

f =30Hz

PSD

( f ) (9)

Four spectral ratios were used to resume the de-

celeration of the EEG as deﬁned in (Rodrigues and

Teixeira, 2011).

RP(α)

RP(θ)

(10)

RP(α) + RP(β

) + RP(β

) + RP(γ)

RP(δ) + RP(θ)

(11)

RP(β

) + RP(β

)

RP(δ)

(12)

RP(β

)

RP(δ)

(13)

In order to achieve a through analysis of the spec-

tral characteristics of the

EEG

records of the patients

diagnosed with probable Alzheimer’s disease and

EEGDiscriminationwithArtificialNeuralNetworks

237

in this study the following parameters were calculated

using the PSD.

The Mean Frequency (

) is deﬁned as the fre-

quency comprising of

50%

of the power (Rodrigues,

2011).

0.5

40Hz

∑

1Hz

PSD

( f ) =

∑

1Hz

PSD

( f ) (14)

The

PSD

shows the highest peak at the

band. So

we can calculate a parameter entitled Individual Alpha

Frequency (

IAF

) in the extended

band between 4Hz

and 15 Hz (Rodrigues, 2011).

0.5

15Hz

∑

4Hz

PSD

( f ) =

IAF

∑

4Hz

PSD

( f ) (15)

The Spectral Edge Frequency (SEF95%) is calcu-

lated as the frequency that comprises 95% of the power

spectrum (Rodrigues, 2011).

0.95

40Hz

∑

1Hz

PSD

( f ) =

SEF95%

∑

1Hz

PSD

( f ) (16)

Finally, the Spectral Entropy (

) is a measure of

disorder that can be used as an irregularity estimate

of the

EEG

as we can see on the equation below (Ro-

drigues, 2011):

SE = −

40Hz

∑

1Hz

PSD

( f ) · Log[PSD

( f )] (17)

3 METHODOLOGY

We developed an

ANN

in order to discriminate

EEG

signals between

and

patients. It was created the

input

matrix with

EEG

data of the subjects in study.

This input matrix consists in one column for each seg-

ment of 5 s for all subjects (controls and patients). The

lines of the matrix consist in the 14 features deﬁned

above (6

, 4 spectral ratios -

IAF

SEF95%

and

) for the 19 electrodes, in a total of 14

∗

19=266

lines. In the future we intend to reduce this number

of input nodes by a selection process. It was found

that some electrodes presented probable noise, due to

bad conduction during the acquisition process. These

electrodes were identiﬁed by observation of the spec-

tral components because they had similar energy in

all bandwidth. To reduce that noise we proceeded to

the average of electrodes without noise belonging to

the same group of the subject involved, including the

damaged electrode. For example, if the electrode 8 of

a control subject presented noise we proceeded to the

substitution of the data of this electrode by the average

of all electrodes of

without noise in any electrode,

including the electrode 8. This method allowed to re-

move electrodes with wrong data. To identify noise

moments we have used the

sur f

function of

Matlab

software, which allowed to create a three-dimensional

shaded surface from

PSD

components of signals of

all ﬁve segments seconds presented in the electrode

signal, which would allow the visualization of some

useful information to create the P matrix.

Thus, in a ﬁrst phase it was checked whether there

were signiﬁcant oscillations between electrodes posi-

tioned at the same position either

patients

and, if oscillations in subjects belonging to the same

group were signiﬁcant. It was found limited relevance

for the identiﬁcation by visualization, because differ-

ences were observed in electrodes of subjects belong-

ing to the same group.

In a second phase we tried to ﬁnd out if there were

amplitude variations along the electrode segments and

if the peaks were determinative. It was found that

patient 4 presented small variations along the segments

in frequency (view Figure 1), compared to other cases

where there were no large amplitudes of the electrodes

and that presented a greater distribution of the am-

plitudes per electrode. It has been assumed that

patient 4 was in an advanced phase of the AD. As in

patient 4, also the patient 10 and 38 and control subject

17 exhibited large variations with small amplitudes of

energy in a restricted range of frequencies. It should

be noted the fact that in the universe of 14

the only

one who showed higher accumulations of energy in a

restricted range of frequencies of electrodes was the

control subject 17.

Figure 1: EEG signal Amplitude of successive segments

(right axis) of one electrode along frequency (left axis).

In a third and last stage we look for noise existence

patients and

and we conﬁrmed noise in elec-

trode 8 of the patient 5, in electrodes 8 and 9 of the

control subject 7, in electrodes 8 and 16 of the control

subject 13, in electrode 16 of patient 25 and in the

electrodes 8,9 and 17 of the patient 50.

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238

The matrix P was split into three randomly exclu-

sive subgroups:

• Subset 1: Training set.

• Subset 2: Validation set.

• Subset 3: Test set.

The subset 1 presented the most part of the

ma-

trix for training. It was about 80% of the

matrix.

Therefore, this subset had the remaining segments for

each subject in study. The subset 2 and 3 showed about

10% of the

matrix, respectively, and they had three

segments of each subject randomly selected, from the

34 subjects in study. The size of each subset can be

observed in table 1.

Table 1: Dimension of Training, Test and Validation Sets.

Total Dimension of matrix P=266x1066

Training Set Validation Set Test Set

266x862 266x102 266x102

In this work a feed-forward architecture of

ANN

was employed with 266 nodes input layer and 1 node

in the output layer. The output node codes if the data

features correspond to a control subject or in oppo-

sition corresponds to a patient. Different number of

nodes in the hidden layer has experimented. For all

ANN

architectures, two learning algorithms were used,

the Resilient Back-Propagation (

trainrp

) and the Lev-

enberg - Marquardt (

trainlm

) and two Transfer Func-

tions in the hidden layer were also used, Logarithmic

Sigmoid (

logsig

) and the Tangential Sigmoid (

tansig

Several nodes variations on the hidden layers were

utilized and the

purelin

was the transfer function of

the output layer. The best

ANN

results were obtained

with

trainlm

logsig

and 9 nodes on the hidden layer,

as can be seen by the autocorrelation coefﬁcient (R)

of the training, test and validation sets and overall

matrix presented in the table 2.

Once the output of the ANN is a linear function

between 0 and 1 and we considered 0 for controls and

1 for

patients a threshold value of 0.5 of output

was considered. Therefore values

0.5 of the output

are interpreted as

patients and values

0.5 are

considered as Cs.

3.1 Train with Resilient

Back-propagation Algorithm

Next will be showed the best results for regression and

for the performance of

ANN

obtained with

trainrp

al-

gorithm (Riedmiller and Braun, 1993),

logsig

transfer

function and 14 nodes in the hidden layer (view Figure

2 and Figure 3). It must be noted that the result of the

correlation coefﬁcient in Test Set has the highest value

for this

ANN

. The

tansig

transfer function had no-

ticed great results but not so greatest as

logsig

transfer

function.

Figure 2: Regression Plot with trainr p algorithm.

Figure 3: Performance Plot with trainrp algorithm.

3.2 Train with Levenberg-Marquardt

Back-propagation Algorithm

Next will be showed the best results for regression and

for the performance of

ANN

obtained with

trainlm

al-

gorithm (Marquardt, 1963; Hagan and Menhaj, 1994) ,

logsig

transfer function and 9 nodes in the hidden layer

of entrance in the neural network (view Figure 4 and

Figure 5). This

ANN

was the performance with the

best correlation coefﬁcient of the

matrix and of the

Training and Validation Sets. Similar to the

trainrp

algorithm, the

tansig

activation function had noticed

equally great results but not so greatest as

logsig

acti-

vation function.

EEGDiscriminationwithArtificialNeuralNetworks

239

Table 2: Classiﬁcation results.

Training Transfer Nodes of the Training Test Set Validation P Matrix

Algorithm Function Hidden Layer Set (R) (R) Set (R) (R)

TrainRP Logsig 14 0.98341 0.96138 0.96171 0.97932

TrainLM Logsig 9 0.99964 0.95758 0.9653 0.99245

TrainRP Tansig 11 0.97885 0.93211 0.95087 0.97173

TrainLM Tansig 9 0.99807 0.91164 0.95555 0.98622

Figure 4: Regression Plot with trainlm algorithm.

Figure 5: Performance Plot with trainlm algorithm.

4 CONCLUSIONS AND FUTURE

WORK

keeps on defying medical research, even despite

the progress achieved in better knowledge of the com-

plex processes of deterioration of memory levels, this

disease still remains with no prospect of a cure. There-

fore, it is very important to have an early diagnose

of disease in order to avoid their evolution. In this

paper we developed a model based on

ANN

to discrim-

inate between

and

patients. A set of 14 features

for each electrode was used in the input of the

ANN

These features are based in the

PSD

determined with

the

ST FT

. The features consists in the Relative Power

of the

PSD

in the bandwidths of delta, theta, alpha,

beta1, beta2 and gamma, four spectral ratios, the Mean

Frequency, the Individual Alpha Frequency, the Spec-

tral Edge Frequency and the Spectral Entropy. The

output consisted in a binary to classify

pa-

tients. Several

ANN

were experimented with different

number of nodes in the hidden layer and activations

functions. The Resilient Back-Propagation and the

Levenberg-Marquardt training algorithms were also

experimented. The best results give a correlation co-

efﬁcient of 0.961 in the test set. Once the data of this

set were never seen during the training process we

may consider it as a very competitive result compared

with similar previous works. Namely, the obtained

classiﬁcation results can be compared with the stud-

ies presented in (Vialatte et al., 2005a; Vialatte et al.,

2005b; Vialatte et al., 2008; Rodrigues et al., 2011;

Rodrigues and Teixeira, 2011; Rodrigues, 2011; Melis-

sant et al., 2005).

For a future work we intend to reduce the number

of features used in the input of the

ANN

and identify

electrodes that can be removed due to low discrimi-

native power. Experiment the use of the

PSD

deter-

mined using the Wavelet Transform as presented by

Rodrigues in (Rodrigues, 2011; Rodrigues and Teix-

eira, 2011; Rodrigues et al., 2011).

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