MULTIFOCAL ELECTRORETINOGRAPHY

Early Detection of Glaucoma based on Wavelets and Morphological Analysis

J. M. Miguel, S. Ortega, I. Artacho, L. Boquete, J. M. Rodríguez

Department of Electronics, University of Alcalá, 28701 Alcalá de Henares, Spain

P. De La Villa

Department of Physiology, University of Alcalá, 28701 Alcalá de Henares, Spain

R. Blanco

Department of Surgery, University of Alcalá, 28701 Alcalá de Henares, Spain

Keywords: Wavelet transforms, Glaucoma, m-sequence, Multifocal electroretinogram, Morphological analysis.

Abstract: This article presents one of the alternative methods developed for the early detection of ocular glaucoma

based on the characterisation of mfERG (multifocal electroretinography) readings. The digital signal

processing technique is based on Wavelets, hitherto unused in this field, for detection of advanced-stage

glaucoma and the study of signal morphology by means of identity patterns for detection of glaucoma in

earlier stages. Future research possibilities are also mentioned, such as the study of orientation in the

development of the disease.

1 INTRODUCTION

Glaucoma is currently deemed to be a high-risk eye

disease since a large percentage of the population

suffer from its effects. The method proposed herein

has been developed for study and analysis of OAG

(open angle glaucoma), the commonest form in

today’s society.

The sheer complexity of the disease and its

occultation make early and reliable detection

essential. The traditional techniques for clinical

analysis of the retina are based on indirect methods

(measurement of the intraocular pressure, visual

inspection of the eyeground, campimetric tests, etc).

Their main drawback is that they do not give

objective information on the functioning of the

retinal photoreceptors (Catalá et al., 2005), essential

elements in the perception of light energy. A new

technique has recently been developed for obtaining

this retina-functioning information in a quick and

reproducible way; this technique is known as the

multifocal electroretinogram (mfERG). The mfERG

enables a functional exploration to be made of the

light sensitivity of the retinal cells and also the

spatial distribution of this sensitivity (J. M. Miguel

et al., 2007). The mfERG basically involves

recording the variations in retinal potential evoked

by a light stimulus and then mapping out the results

in a 2D or 3D diagram showing those regions that

respond to the visual stimuli (Sutter & Tran, 1992)

(Sutter EE., 2001).

The mfERG technique allows simultaneous

recording of local responses from many different

regions of the retina, building up a map of its

sensitivities. As in the conventional

electroretinogram (ERG), also called the full-field

electroretinogram, the potential is measured as the

sum of the electric activity of the retina cells. In the

full-field ERG, however, the signal recorded comes

from the whole retina surface, so it is hard to detect

smaller one-off defects that do not affect the whole

retina. The mfERG, by contrast, gives detailed

topographical information of each zone and can

therefore detect small-area local lesions in the retina

and even in its central region (fovea) (D. C. Hood et

al., 2003).

From the technical point of view, equipment is

needed for capturing the visually evoked potentials

M. Miguel J., Ortega S., Artacho I., Boquete L., M. Rodríguez J., De La Villa P. and Blanco R. (2009).

MULTIFOCAL ELECTRORETINOGRAPHY - Early Detection of Glaucoma based on Wavelets and Morphological Analysis.

In Proceedings of the International Conference on Biomedical Electronics and Devices, pages 17-23

DOI: 10.5220/0001430300170023

 SciTePress

at retina level (presented as a set of hexagons of

varying sizes and intensities). Due to the low

amplitude of the signals generated (down to

nanovolt level), the technique calls for suitable

hardware equipment (recording electrodes,

instrumentation amplifiers, digitalisation, etc) and

also signal processing algorithms (filtering,

averaging or smoothing procedures, rejection of

artefacts, etc) to ensure that the results are clinically

useful (M. F. Marmor et al., 2003).

This paper gives a description of the recording

and arrangement of the signals we have used in our

research, the signal analysis by the Wavelet

transform for recording possible advanced-stage

glaucoma markers, the detection of smaller lesions

by means of morphological analysis of the signal; it

also mentions possible future research lines.

2 METHODS

2.1 Obtaining the Signals

A total of 50 patients with diagnosis of advanced

open angle glaucoma (OAG) as well as an identical

number of healthy subjects were included in our

mfERG record database, used for obtaining markers

by means of the wavelet transform. Moreover, to

study the efficiency of our morphological analysis, a

second database was drawn up formed by 15

patients diagnosed with early-stage Glaucoma plus

an identical number of healthy controls.

The signal recording system was the VERIS 5.1

multifocal recording system (Electro-Diagnostic

Imaging, San Mateo, USA). The stimulus consisted

of an m-sequence applied to a group of 103

hexagons, as shown in figure 1, displayed on a 21-

inch monitor and covering a 45º arc of the retina.

The local luminance of each hexagon was 200 cd/m

in the on phase and less than 1.5 cd/m

in the off

phase, determined by the pseudorandom sequence.

The monitor frequency was 75 Hz and the m-

sequence was modified so that each step was

followed by 4 frames in the following order: flash-

dark-flash-dark, as shown in figure 2. In the flash

frames all the hexagons were illuminated with a

maximum luminance of 200 cd/m

, with a minimum

luminance of less than 1.5 cd/m

in the dark frames.

The background luminance of the rest of the monitor

surface surrounding the hexagons was held steady at

100 cd/m

. This stimulation protocol is especially

adapted for obtaining responses from the retinal

ganglion cells and their axons (Hagan R. P. et al.,

2006). It is based on the effect of the focal responses

(M) on the following global stimulus (F), which

amplifies the signals coming from the ganglion cells.

Figure 1: Geometry of the multifocal stimulus and

regrouping of the hexagons.

Basically, the protocol (M-F-O-F-O) consists of

five steps. In the first step (M) each hexagon follows

a luminous stimulation (200 cd/m

) determined by a

pseudorandom binary m-sequence. In the second

step the whole area is illuminated (200 cd/m

) (F),

followed by a dark sequence (O) (<1.5 cd/m

followed by another global flash (200 cd/m

) (F) and

then darkness again (O) (<1.5 cd/m

). This

stimulation will give us an acceptable signal-to-

noise ratio and also ensures a reasonably short

recording time (9 minutes).

Figure 2: Modification of the m-sequence.

The stimulus was displayed through

pharmacologically dilated pupils (minimum

diameter of 7 millimetres) using a Burian-Allen

bipolar contact lens (Hansen ophthalmics, Iowa

City, IA). Contact lens adaptation was facilitated by

a drop of topical anaesthetic (0.5% Proparacaine).

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The residual spherical refractive error was corrected

by the VERIS™ autorefractor, mounted on the

stimulation monitor. The alignment of the patient’s

pupil with the monitor optic and the fixation stability

are controlled by an attached infrared camera. Each

monocular recording lasts about 9 minutes

(exponent of the stimulation m-sequence = 13). To

make the process more comfortable for the patient,

the recording process was divided into eighteen 30-

second segments. Segments contaminated with

ocular movements were discarded and recorded

anew. The signals are amplified with a Grass

Neurodata Model 15ST amplification system (Grass

Telefactor, NH), with a 50,000 gain, filters with 10-

300 Hz bandwidth and a sampling interval of 0.83

milliseconds (1200 Hz).

Each participant was given a complete

ophthalmic exam, including general anamnesis, best-

corrected visual acuity, slit lamp biomicroscopy,

intraocular-pressure measurement using the

Goldmann applanation tonometer, gonioscopy,

dilated fundoscopic examination (90D lens), stereo

retinographs and a 24-2 SITA Humphrey automated

perimetry (Swedish Interactive Threshold

Algorithm. Carl Zeiss Meditec Inc.). A diagnosis of

open angle glaucoma was established where there

were at least two consecutive abnormal visual fields

in the Humphrey campimetry, (threshold test 24-2),

defined by: 1) a pattern standard deviation (PSD)

and/or corrected pattern standard deviation (CPSD)

below the 95% confidence interval; or 2) a

Glaucoma Hemifield Test outside the normal limits.

We define as abnormal an altitudinal hemifield in

the Humphrey visual field analysis giving three or

more contiguous sectors below the 95% confidence

interval, with at least one of them below the 99%

confidence interval. The visual field was dismissed

as unreliable if the rate of false positives, false

negatives or fixation losses was higher than 33%. A

control database was also established on the basis of

normal eye records established within the

longitudinal prospective study. All these normal eye

records had an intraocular pressure of 21 mmHg or

less (with no previous history of ocular

hypertension). An ophthalmic examination of the

optic papilla was also conducted to check that it fell

within the normal structural parameters.

The signals obtained from the 103 hexagons

were regrouped and averaged to build up a new 56-

sector map as shown in figure 1. The purpose of this

regrouping was to simplify the analysis and to

improve the signal-to-noise ratio. A 56-sector

topography was therefore chosen, similar to that

studied in automated campimetry, the clinical ¨gold-

standard¨ for evaluating the visual field. It should

also be noted here that sector 41 is the average of a

greater number of hexagons, since it is the area

containing the blind spot and, as such, more difficult

to analyse.

Two mfERG record databases were built up, one

containing healthy or control individuals and the

other glaucoma-affected individuals for study by

means of the Discrete Wavelet Transform (DWT).

Two other specific databases were also created to be

studied by means of an alternative technique,

Morphological Analysis, all made up by a complete

56-sector map as shown in figure 1.

Not all the sectors making up the map to be

analysed by the Wavelet Transform belonged to a

single patient; the map groups together 56 clearly

glaucoma-identified sectors from among the fifty

patients diagnosed with the same symptom.

Following a similar procedure, a sector map

comprising the control database was built up, this

time on the basis of healthy individuals.

As regards the databases used for the

morphological analysis, these were made up by two

15-record collections from the 56 sectors: the first

coming from 15 patients affected with early-stage

OAG and showing between 3 and 12 diseased

sectors, and the other built up from the 15 healthy

control subjects.

2.2 Study of Severe Lesions by Wavelet

Analysis

DWT was better than morphological analysis as a

mfERG-record analysis tool for detecting severe

retina lesions. Conversely, morphological analysis

was much more efficient for detecting early-stage

glaucoma by extracting certain markers present in

the records.

The great drawback of the Fourier transform-

based analysis is that the time information is

forfeited when the signal is transformed into the

frequency domain. The drawback is particularly

telling when the signal to be analysed is transitory in

nature or of finite duration, as in the case of mfERG

signals, whose frequency content changes over time.

The discrete wavelet transform (DWT) surmounts

this drawback by analysing the signal in different

frequencies with different resolutions, using regions

with windowing of different sizes and obtaining a

two-dimensional time-frequency function as a result.

Wavelet analysis uses finite-length, oscillating, zero-

mean wave forms, which tend to be irregular and

asymmetrical. These are the windowing functions

called mother wavelets. In principle there may be an

MULTIFOCAL ELECTRORETINOGRAPHY - Early Detection of Glaucoma based on Wavelets and Morphological

Analysis

infinite number of possible waves that are eligible

for use as wavelets, but in practice a more limited

number of wavelets are used, of well-known

characteristics, efficacy and implementation: Haar,

Daubechies, Coiflets, Mexican Hat, Symlets, Morlet,

Meyer, etc. In the study we are dealing with here a

great number of them were explored; it was with the

Bior3.1 wavelet that the best subjective results were

obtained for visual identification of certain markers

that help us to differentiate normal mfERG signals

from those belonging to subjects with advanced

glaucoma (J. M. Miguel et al., 2008).

The signal to be analysed is decomposed on the

basis of shifted and dilated versions of the mother

wavelet or analysing wavelet that we have decided

to use; this is all done by means of the correlation

between the signal to be decomposed and the

abovementioned versions of the mother wavelet.

Mathematically, the discrete wavelet transform

(DWT) is defined as:

( , ) ( )2 (2 )

Cjk fn n k

−−

∈

=−

∑

(1)

where the resulting C(j,k) is a series of coefficients

indicating the correlation between the function f(n)

to be decomposed and the wavelet ψa,b(t) dilated to

a scale a=2

and with a shifting b=k2

, with j,k Є Z.

The resulting C(j,k) includes time and frequency

information of the function f(n), according to the

values of j and k, respectively. In practice we obtain

two sets of time-function signals, one of them made

up by the signals A

to A

which represent

successive approximations of increasing smoothness

or declining frequency of the signal f(n), and the

other by D

to D

which represent the successive

details, also of falling frequency.

The signals were analysed by applying up to 5

levels of wavelet decomposition to each one of the

different sectors and for two different time windows:

one from 10 to 190 ms and another from 60 to 90

ms. The first contains the global response to the

multifocal stimulus used here and the second

contains the most important information on the

induced response generated by this type of stimulus.

Several superimposed records were obtained from

different sectors to obtain an overview of the

markers that might differentiate normal signals from

abnormal signals.

2.3 Study of Slight Lesions by

Morphological Analysis

The mfERG readings from patients with early-stage

glaucoma, with slight lesions or isolated sectors

developing the disease, do not show a uniform

pattern over the healthy or diseased retina sectors.

This makes the analysis thereof more critical. To

detect lesions of this type a morphological signal

study was conducted in the IC time interval (induced

component) falling between P1 and P2, as shown in

figure 3.

Figure 3: Morphology of the mfERG signal from one

sector.

Although the claim cannot be made across the

board for all cases, there is usually a series of

morphological characteristics held in common in the

records of healthy sectors, differentiating them from

the diseased ones. These are called identity patterns

(Brad et al., 2002). The identity pattern of the

healthy sectors shows little variation and contains a

quick signal response in and near the induced

component, thus building up more energy at mid

frequencies. This conduct reflects the behaviour of

the healthy retina cells, which tend to respond

quickly and efficiently to the mfERG stimulus. The

behaviour of a glaucomatous sector, on the other

hand, shows much more high frequency oscillatory

potentials in the IC interval, with a more blurred

definition of signal peaks and troughs and a long

drawn-out response. Given the signal characteristics

in said interval, our morphological analysis studies

the behaviour of the following signal parameters:

 Localisation of points P1, N1 and P2.

 Distance between P1 and P2.

 Sample width at N1.

 Slope in the interval N1 - P2.

 Signal oscillations in the interval N1 - P2.

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The waveform of the mfERG reading changes

from one sector to another, depending on the retina

position of each one. To allow for this effect the

analyses have been carried out under different

performance parameters, depending on the sector’s

position in the retina. Results show that the

individualised study of each sector zone gives our

method an enhanced spatial resolution.

3 RESULTS

In the DWT analysis, several superimposed records

were obtained from different sectors to obtain an

overview of the markers that might differentiate

normal signals from abnormal signals.

Figure 4: Detail D4 of the wavelet decomposition for 10

normal sectors (top) and 10 glaucomatous (bottom).

The top graph of figure 4 shows superimposed

the D4 details of the Wavelet decomposition

between 10 and 190 ms from ten different sectors

corresponding to various healthy individuals. The

bottom graph of the same figure shows a similar

representation for ten glaucomatous sectors and with

an identical topographical position to the former.

One of the most obvious features here is that the

signals corresponding to healthy individuals show

their greatest negative edge at about 70 ms, while

signals in the hexagons affected by glaucoma tend to

bottom out at about 45 ms. The efficiency of this

marker was quantified against a time window

running from 25 to 90 ms, looking for the greatest

negative edge. When this edge came in the first half

of the window the signal was classified as

glaucomatous, while if it came in the second half it

was classified as healthy.

Figure 5: A2 approximation of the wavelet decomposition

for 10 normal sectors (top) and 10 glaucomatous (bottom).

Figure 5 (top) shows superimposed the A2

approximations corresponding to the wavelet

decomposition between 60 and 90 ms of ten

different hexagons belonging to different healthy

individuals. The lower part of this figure shows a

similar representation for ten hexagons affected with

glaucoma and with the same topographical position

as those above. In this case a trough appears at about

73 ms for healthy signals, coming slightly later for

abnormal subjects. Since there might be more

troughs, the efficiency of this second marker is

quantified against a time window running from 65 to

MULTIFOCAL ELECTRORETINOGRAPHY - Early Detection of Glaucoma based on Wavelets and Morphological

Analysis

87 ms., seeking this trough. When the trough comes

in the first half of the window the signal was

classified as healthy, while if it came in the second

half it was classified as glaucomatous.

Table 1 shows the results, using both markers

separately, for true and false healthy and

glaucomatous out of a set of 56 sectors belonging to

different healthy individuals and 56 with glaucoma.

Table 1: Results obtained using DWT markers separately

(M=Marker, TH= True Healthy, FG=False Glaucomatous,

TG= True Glaucomatous, FH=False Healthy).

M TH FG TG FH

D4 55 1 48 8

A2 54 2 51 5

The morphological analysis of slight lesions

shows that the duration of the N1 interval is less in

healthy than in glaucomatous sectors, the time-lag of

P2 behind P1 is less in healthy than in glaucomatous

sectors, the amplitude of P2 has to be positive, the

glaucomatous signal shows greater sensitivity in P2

than in N1 and in P1 (accepting a 2% variation).

The disease also shows a change in the

deterioration of healthy sectors according to whether

the lesion is slight or severe (see Figure 6 from left

to right). This evolution can be seen in P2, changing

from a healthy sector morphology with a sharp P2

peak rising quickly from N1, to a flat morphology

with high frequency alterations in P2 (slight case)

and lastly to an even flatter P2 morphology (severe

case). The study’s statistical results are shown in

table 2.

Table 2: Results of the morphological study TH= True

Healthy, FG=False Glaucomatous, TG= True

Glaucomatous, FH=False Healthy).

TH FG TG FH

80 % 20 % 90 % 10 %

Figure 6: P2 wave morphology trend.

4 CONCLUSIONS

The morphology of the signals recorded in each

hexagon varies according to the position that this

hexagon occupies in the retina and the type of

stimulus used. It is also known that the optic nerve

head component (ONHC) is the main cause of the

asymmetries in the records (Brad et al., 2002) (Wei

et al., 2007), whereby said component arrives in

each hexagon with a different time-lag depending on

the distance between the hexagon and the optic

nerve. This will enhance or cancel out some

components as a result of the different retina levels

below the hexagon under study. Loss of the ONHC

has already been mooted as an early indicator of

glaucoma (Nalini et al., 2006) (D. C. Hood, 2000),

so there is obviously a need for adjustment of the

various time windows and types of markers used in

this study, according to the position of the hexagon

in the retina map, to optimise and fine tune the

results obtained herein.

A more in-depth investigation needs to be

carried out to adjust the parameters obtained herein

by means of DWT analysis, to find out best values in

terms of the retinal quadrants and rings to which the

sector under study belongs, in view of the

abovementioned hexagon dependency.

The type of markers used herein and the tool

used to obtain them, i.e., the Wavelet transform,

make it impossible a priori to establish any

association with a specific physiological origin,

since there are no precedents to go on. It does not

fall within the remit of this study to establish a

physiological cause-effect relationship for the

marker but rather to search for technical tools to help

experts to diagnose glaucoma in humans in its early

stages of development.

It is obvious that a joint and complementary use

of all the techniques studied herein would be the best

way to improve OAG diagnosis. In this way the

sectors detected as healthy in the Wavelet study

would be introduced into the signal morphology

analysis to check whether there might be any slight

lesions that Wavelet analysis was incapable of

picking up.

ACKNOWLEDGEMENTS

This work was supported by grants from Comunidad

de Madrid-Universidad de Alcalá (ref. nº CCG06-

UAH/BIO-0711) and Ministerio de Educación y

Ciencia (ref. nº SAF2004-5870-C02-01) awarded to

Pedro de la Villa.

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MULTIFOCAL ELECTRORETINOGRAPHY - Early Detection of Glaucoma based on Wavelets and Morphological

Analysis