Expert Systems with Applications 31 (2006) 199–205
www.elsevier.com/locate/eswa
Classification of electro-oculogram signals using artificial neural network
Ayşegül Güven a, Sadık Kara b,*
b
a
Department of Electronics, Civil Aviation College, Erciyes University, 38039, Kayseri, Turkey
Department of Electrical and Electronics Engineering, Erciyes University, 38039 Kayseri, Turkey
Abstract
This research is concentrated on the diagnosis of subnormal eye through the analysis of Electrooculography (EOG) signals with the help of
Artificial Neural Network (ANN). Multilayer feed forward ANN trained with a Levenberg Marquart (LM) backpropagation algorithm was
implemented. The designed classification structure has about 94.1% sensitivity, 93.3% specifity and positive prediction is calculated to be 94.1%.
The end results are classified as normal and subnormal eye. Testing results were found to be compliant with the expected results that are derived
from the physician’s direct diagnosis. The benefit of the system is to assist the physician to make the final decision without hesitation. With the
future evolution of this system tested on a more populated subject groups, there is always potential for on-line implementation as an auxiliary
diagnostic tool on the Electrophysiology machines.
q 2005 Elsevier Ltd. All rights reserved.
Keywords: EOG; ANN; Subnormal eye
1. Introduction
The human eye is a spherical structure with a radius of about
12 mm. The inside is filled with a transparent substance corpus
vitreum. The retina covers the inside surface and holds the
optically sensitive nerve ends. Furthermore, the retina holds
nerve fibers which leave the eye through the optical disc
(Sprelckelsen Grumstup, Johnsen, & Hensen, 1994).
The eye has a standing electrical potential or charge across
it, like a weak battery, with the front of the globe positive and
back negative. The resting or ‘standing potential’ is generated
largely by the trasepithelial potential across retinal pigment
epithelium (RPE). It varies from one to several milivolts,
depending upon the state of ambient retinal illumination,
because light leads to a polarization of the basal pigment
epithelial membrane that translates into changes in the
transepithelial potential. Retinal illumination causes an initial
rapid fall in the standing potential over 60–75 s (the ‘fast
oscillation’) followed by a slow rise over 7–14 min. (the ‘light
response’ or ‘slow oscillation’). The clinical electro-oculogram
(EOG) measures the amplitude of the standing potential and
* Corresponding author. Tel.: C90 352 4374901 32228; fax: C90 352
4375784.
E-mail addresses: aguven@erciyes.edu.tr (A.̧ Güven), kara@erciyes.edu.tr
(S. Kara).
0957-4174/$ - see front matter q 2005 Elsevier Ltd. All rights reserved.
doi:10.1016/j.eswa.2005.09.017
light response (Heckenlively, & Arden, 1991; Marmor, &
Zrenner, 1993).
In the EOG, the standing potential is measured indirectly,
using the fact that the spatial orientation of a polarized eye is
detected by skin electrodes placed nasal and temporal to the
eye. Much of the changes in standing potential, however, can
be monitored via the EOG. (Marmor & Zrenner, 1993)
The clinical value of the EOG for diagnoses as well as
following the course of certain diseases has been studied in
retinitis pigmentosa, diffuse RPE disease, Best’s macular
dystrophy, Stargardt’s disease, dominant drusen and chloroquine toxicity (Heckenlively, & Arden, 1991; Tasman, 1992).
In biomedicine, the assessment of vital functions of the body
often requires noninvasive measurements, processing and
analysis of physiological signals. Examples of physiological
signals found in biomedicine include the electrical activity of
the brain—the electroencephalogram (EEG), the electrical
activity the heart—the electrocardiogram (ECG), the electrical
activity the eye—i.e. electroretinogram (ERG) and electrooculogram (EOG)—respiratory signals, blood pressure, temperature and speech signals (Lisboa, Ifeachor, & Szczepaniak,
2000).
Often, biomedical data are not well behaved. They vary
from person to person, are affected by factors such as
medication, environmental conditions, age, weight, mental
and physical state. Consequently, clinical expertise is often
required for a proper analysis and interpretation of medical
data. This has led to the integration of signal processing with
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A. Güven, S. Kara / Expert Systems with Applications 31 (2006) 199–205
Fig. 1. EOG response sample with the components labeled. In the dark phase, the lowest potential (dark trough-Dt) is reached in 8–12 min. A bright light is then
turned on and recordings continue at 1-min intervals with the potential increasing in amplitude until the highest point (light peak-Lp) is reached in 7–10 min.
intelligent techniques such as neural networks, expert systems
and fuzzy logic to improve performance (Lisboa et al., 2000).
Our primary research motivation was to advance the
research of subnormal (macular degeneration) eye and develop
a novel decision making system for identification of this
subnormalities. We employed the Artificial Neural Network
(ANN) for the purpose of distinguishing between subnormal
and normal eye. We have implemented an ANN that will not
only simplify the diagnosis but also enable the physician to
make a quicker judgment about the existence of eye disease
more confidence. An ANN can determine its conditions and
adjust itself to provide different responses by using inputs and
desired outputs. The most important thing about an ANN is that
it works as an expert system which will eventually help the
physicians with the decision making process about the
existence of subnormalities.
Applications of ANNs in the medical field are numerous.
The numerous applications exhibit the suitability of ANNs in
pattern classification including diagnosis of diseases (Übeyli,
& Güler, 2003), for example, photoelectric plethysmography
pulse waveform analysis (Allen, & Murray, 1993), diagnosis of
myocardial infarction (Baxt, 1990; Baxt, 1995), electrocardiogram analysis (Edenbrandt, Heden, & Pahlm, 1993; Hilera,
Martinez, & Mazo, 1995), analysis of doppler shift signals
(Güler, & Übeyli, 2003), differentiation of assorted pathological data (Dybowski, & Gant, 1995; Miller, Blott, & Harries,
1992) and electroretinogram classification (Lipoth, Hafez, &
Goubran, 1991); however, neural network analysis of EOG
signals is a relatively new approach.
This research is concentrated on the diagnosis of subnormalities through the analysis of EOG signals with the help
of an ANN. Most of the research in this area concentrative on
auxiliary systems that will assist the physician with the
decision making part of the diagnosis. The aim is to help the
ophthalmologist interprent the output of the examination
systems more easily and diagnose the problem more accurately
(Lisboa et al., 2000).
2. Material and methods
EOG signal acquisition was conducted by a Tomey Primus
2.5 electrophysiology unit in the Ophthalmology Department
of Erciyes University Hospital. Representative EOG signal
wave is seen in Fig. 1. The system of the block diagram of the
set up is seen in Fig. 2.
The test group consisted of 72 people composed of 32
normal and 40 subnormal eye subjects. The patient group
having eye disease had an age range of 30–78 years (Mean
ageZ52) and involved 18 male and 22 female patients. There
were 14 male and 18 female people. The group had a mean age
of 34 years ranging from 26 to 55 years.
Fig. 2. Block diagram of the set up.
A. Güven, S. Kara / Expert Systems with Applications 31 (2006) 199–205
201
2.1. Measurement of the EOG
The importance of the clinical measurement of the EOG lies
in the fact that the amplitude of the responce changes when
certain luminance conditions are varied. Thus, if a patient is
placed in darkness the potential will decrease, while if a bright
light is put on following dark adaptation the potential will
increase (Fig. 1). The relative ratio of amplitudes in dark and
light gives an index of the RPE response to ionic changes. This
ratio of the light-peak/dark-trough (Lp/DtZArden ratio)
normally is 1.8 or greater. A ratio of 1.5 or less indicates a
clearly abnormal RPE response. Generally a ratio of about 1.6
is considered the demarcation between normal and abnormal
(Tasman, 1992).
To standardize the test and maximize the response a specific
protocol is used. Following a preadaptation period of several
minutes to acquaint the patient with the test, the lights are
turned off for 15 min. Recordings for several saccades are made
in the dark at 1 min intervals. The lowest potential (dark
trough-Dt) is reached is 8–12 min. A bright light is then turned
on and recordings continue at 1-min intervals with the potential
increasing in amplitude until the highest point (light peak-Lp)
is reached in 7–10 min (Fig. 1) (Heckenlively, & Arden, 1991;
Marmor, & Zrenner, 1993; Tasman, 1992).
This electrical response is generated by the retinal pigment
epithelium with the light peak being generate this potential it is
likewise necessary to have intact photoreceptors which are
apposed to the RPE (Tasman, 1992).
Fig. 3. (a) Comparison of Dt values for the normal (32 person) and subnormal
(40 person), (b) Comparison of Lp values for the normal (32 person) and
subnormal (40 person).
2.2. Preparation of testing and training files
The data which is recorded from all patients was used as
input of the ANN. After the detection of the Dt and Lp values,
feature extraction process is started. It can be seen in Fig. 3
where a comparison of amplitudes of the Dt and Lp EOG
signals input values of ANN for the normal (32 person) and
subnormal (40 person) eye. It is seen that, majority magnitudes
of the Dt and Lp EOG signals similar to each other (normal and
subnormal). These values were used as an input data for
multilayer perceptron (MLP). During supervised learning, the
ANN was trained on input vectors and the target output vectors
with which it is required to associate the input vectors. The
outputs are represented by unit basis vectors:
[0 1] normal
(Edenbrandt et al., 1993) subnormal
Output vectors were determined from realized clinic
findings these pattern electroretinography (PERG), angiography and Arden ratio of EOG.
Fig. 4. The algorithm of the classification system.
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A. Güven, S. Kara / Expert Systems with Applications 31 (2006) 199–205
Table 1
An example of a training set consists of random selected subjects
Ann inputs
Normal eyes
Subnormal eyes
Subject no
1
2
Subject no
1
3
8
11
15
18
23
28
37
40
184.33
526.12
208.74
672.61
139.16
511.47
292.97
625.00
421.14
917.97
391.85
481.97
253.71
312.06
506.85
684.24
429.72
550.04
363.86
520.31
2.3. The procedure of the classification system
Fig. 4 shows the procedure used in the development of the
classification system. It consists of four parts: (a) measurement
of EOG signals, (b) neural network inputs were selected, (c)
classification using neural network, (d) classification results.
2.4. Artificial neural networks
An ANN is a mathematical model consisting of a number of
highly interconnected processing elements organized into
layers, the geometry and functionality of which have been
likened to that of the human brain. An ANN is trained with the
available data samples to explore the relation between inputs
and outputs, so that you can reach the proper and accurate
outputs when you input some new data (Simpson, 1989).
A Multilayer feed forward ANN was implemented in the
MATLAB software package (MATLAB version 5.3 with
neural network toolbox). This choice is appropriate for solving
pattern classification problems where supervised learning is
implemented with a LM backpropagation algorithm. (An
example of training set is seen in Table 1) A LM back
propagation neural network was used for the interpretation of
eye EOG waveforms. The advantage of using this type of ANN
is the rapid execution of the trained network, which is
particularly advantageous in signal processing applications.
ANN training is usually formulated as a nonlinear least-squares
problem.
2.4.1. Levenberg marquard backpropagation algorithm
The backpropagation algorithm is a widely used training
procedure that adjusts the connection weights of a MLP
(Rumelhart, Hilton, & Williams, 1986). Essentially, the LM
algorithm is a least-squares estimation algorithm based on the
maximum neighborhood idea. As shown in Fig. 5, an MLP
consists of three layers: an input layer, an output layer, and one
or more hidden layers. Each layer is composed of a predefined
number of neurons. The neurons in the input layer only act as
buffers for distributing the input signals xi to neurons in the
hidden layer. Each neuron j in the hidden layer sums up its
input signals xi after weighting them with the strengths of the
respective connections wij from the input layer, and computes
its output yj as a function f of the sum:
sigmoidal, hyperbolic tangent, or radial basis function. The
output of neurons in the output layer is similarly computed
(Beale, & Jackson, 1990; Güler, & Übeyli, 2003; Haykin,
1994; Türkoğlu, Arslan, & İlkay, 2002; Wright, & Gough,
1999).
Training a network consists of adjusting the network
weights using the different learning algorithms. A learning
algorithm gives the change Dwij(t) in the weight of a
connection between neurons i and j at time t. For the
Levenberg-Marquardt learning algorithm, the weights are
updated according to the following formula
wij ðt C 1Þ Z wij ðtÞ C Dwij ðtÞ
with
Dwij Z ½JT ðwÞJðwÞ C mIK1 JT ðwÞEðwÞ
where J is the Jacobian matrix, m is a constant, I is a identity
matrix, and E(w) is an error function (Allen, & Murray, 1993;
Baxt, 1990; Lisboa et al., 2000; Übeyli, & Güler, 2003).
2.5. Classification using neural network
ANN underwent supervised learning to perform successful
pattern recognition of the EOG signals. With sufficient
training, the ANN should be able to classify correctly
previously unseen input vectors. The network is iterated for
single and double hidden layers with combinations of 1–10
neurons in each layer (Fig. 5). For each layer combination, the
target mean square error was set to 0.1!10K4 and the epoch
yj Z f ðSwij xi Þ
where f is the activation function that is necessary to transform
the weighted sum of all signals impinging onto a neuron. The
activation function f can be a simple threshold function, a
Fig. 5. Structure of ANN.
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203
Table 2
Test results
Group type
Real number of subjects in the test group
True
False
Negative (normal)
Positive (subnormal)
Total
SensitivityZ(TP)/(TPCFN) %Z%94.1
SpecifityZTN/(TNCFP) %Z%93.3
Positive predictivityZTP/(TPCFP) %Z%94.1
TP: true positive TN: true negative
FP: false positive FN: false negative
15
17
32
14(%93.3)
16(%94.1)
30(%93.75)
1(%6.7)
1(%5.9)
2(%6.25)
number was taken as 500. The train input data set consisted of
17 normal and 23 subnormal eye patients, while the test data set
was made of 15 normal and 17 subnormal eye patients. The
minimum training and testing errors were accomplished with
the combination of single hidden layer consisting of five
neurons. Hidden layer sigmoidal function and output layer
linear function was used.
The performance of the ANN algorithms was assessed by
the following measures;
(1) True Positive (TP): the ANN identifies an input as a
subnormal eye that was labeled as a patient by the test.
(2) True Negative (TN): the ANN identifies an input as a
normal that was labeled as a healthy by the test.
(3) False Positive (FP): the detection of a subnormal in an
EOG and clinical findings that was labeled as a normal by
the test.
(4) False Negative (FN): the detection of normal in an EOG
and clinical findings that was labeled as a patient by the
test.
The performance of the classifier is also assessed in terms of
sensitivity and specificity as follows;
(1) Sensitivity: a measure of the ability of the classifier to
detect subnormal eye.
Subjects having eye diseases are classified correctly with
94.1% and incorrectly with 5.9%. In this case, by using this
network classification, normal subjects and patient subjects are
classified with 93.3 and 94.1% accuracy, respectively.
The sizes of mean square error (MSE) and mean absolute
error (MAE) can be used to determine how well the network
output fits desired output, a testing MSE of 0.0310 and MAE of
0.0508 was observed for our optimized MLP feed forward
network with a training MSE of 5.5665!10K5 and MAE of
0.0048. The variation of system error rate with respect to the
epoch number during training iterations is shown in Fig. 6. As
seen in this figure, the results are stable and no fluctuations are
observed. This indicates that the selected parameters are most
proper set for a minimum rate. As seen in Table 2, 93–94%
success rate of classification was accomplished with the
designed feature extraction and the neural network structures.
Final results were classified as normal and subnormal. There
was one false classification in the negative group, while 14
subjects were correctly recognized as healthy. In the positive
(subnormal eye) group, only one subject was misclassified, and
16 people were accurately classified as diseased. The overall
results, shows that 93.75% correct classification was achieved,
whereas two false classifications have been observed for the
group of 32 people in total. With these results (the values of
statistical parameters), this network has about 94.1%
Sensitivity Z ðTPÞ=ðTP C FNÞ%
(2) Specificity: a measure of the ability of the classifier to
specify normal eye.
Sensitivity Z ðTNÞ=ðTN C FPÞ%
(3) Positive predictivityZ ðTPÞ=ðTPC FPÞ%
3. Results
After the training phase, testing of the LM backpropagation
neural network was established. The data, which has not been
used as an input to the network, was applied to the network for
testing the network performance.
After the process, normal (healthy) subjects are classified
correctly with 93.3% and incorrectly with 6.7% (Table 2).
Fig. 6. Variation of the error rate with respect to the epoch number in the ANN.
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Fig. 7. (a) Desired output result (normal), actual network output result (normal), (b) Desired output result (subnormal), actual network output result (subnormal).
sensitivity, 93.3% specifity and positive prediction is calculated to be 94.1% (Table 2).
The ANN test results belong to normal and subnormal
patients were compared realized clinic findings (Pattern
electroretinography (PERG), angiography and Arden ratio of
EOG) belong to same persons. The outputs of actual network
and desired network are set to vary within the range of 0–1. In
Fig. 7(a), the output values of 15 subjects shows that all the
normal subject values are one. The actual network output
pattern characterized around 1 except one, which has the value
0. This means that one of 15 subjects was misclassified in the
network. On the other hand, Fig. 7(b) illustrates the desired
output values of 17 subjects are 1 and the actual network
outputs are around 1 while one subject has the output value of
0. From this perspective, it explains that one of 17 subjects was
misclassified in the network.
4. Discussion and conclusion
The fuzzy appearance of the EOG signals sometimes makes
physicians suspicious about the existence of eye diseases and
causes false diagnosis. Our technique gets around this problem
using ANN to decide and assist the physician to make the final
judgment in confidence.
The three layer MLP structure that we have built gave very
promising results in classifying the normal and subnormal
eyes. We are not claiming to replace the currently used devices
for EOG. On the other hand, we are proposing a complimentary
system that can be coupled to software of the ophthalmic
electrophysiology devices. The benefit of the system is to assist
the physician to make the final decision without hesitation. But,
the limitation of this system that the system has had 6.25%
misclassified result in the test since we selected some of data
used from early phase of the macular degeneration.
In this study, we believe that this research developed an
expert system for the interpretation of the EOG signals using
ANN. The stated results show that the proposed method can
make an effective interpretation.
Acknowledgements
This project was supported as Post-Graduate Education
and Research Project by Erciyes University (Project no. FBT04-27).
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205
Ayşegül Güven graduated from Electronics Engineering Department of
Erciyes University with a BS degree in 1999 and is currently employed as an
instructor at the department of electronics in the Civil Aviation School.
Consequently, she is pursuing her PhD degree in Department of Electronics
Engineering at Erciyes University and conducting her research in the area of
biosignal processing and neural network applications in medicine.
Sadık Kara graduated from the Faculty of Electronics Engineering, Erciyes
University in 1988. He received his MS degree in 1991 and doctorate in 1995
from the Institute of Science, Erciyes University. He is presently working as an
Associate Professor in the Bioelectronics branch of Electronics Engineering at
the same institution. His general research interests include bioinstrumentation,
biosignal processing and neural network applications in medicine.
ID
389065
Title
Classificationofelectro-oculogramsignalsusingartificialneuralnetwork
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