J. Biomedical Science and Engineering, 2010, 3, 816-821 JBiSE
doi:10.4236/jbise.2010.38109 Published Online August 2010 (http://www.SciRP.org/journal/jbise/).
Published Online August 2010 in SciRes. http:// www.scirp. org/journal/jbise
Use of instantaneous energy of ABR signals for fast detection
of wave V
Adeela Arooj, Mohd Rushaidin Muhamed, Sheikh Hussain Shaikh Salleh, Mohd Hafizi Omar
Center for Biomedical Engineering, Faculty of Biomedical and Health Science Engineering, University Technology Malaysia, UTM
Skudai, Johor, Malaysia.
Email: dradeelaarooj@yahoo.com
Received 16 December 2009; revised 28 January 2010; accepted 31 May 2010.
Brainstem evoked response audiometry is a powerful
diagnostic technique in audiology, otology and neuro-
logy. ABR(Auditory Brainstem Response) machine
has been very useful and popular in past two decades
for detection of hearing defects and pathologies in
newborns and children. In ABR, wave V is the most
prominent and robust wave that has been used as
indicator of hearing loss. However, a fast detection of
the wave V is necessary in order to implement new-
born hearing screening. Researchers have intro-
duced different kind of signal processing technique
in order to achieve this target and one of this is Fast
Fourier Transform (FFT) and Wavelet Transform.
In this study, the instantaneous energy of ABR sig-
nal had been introduced as a marker to identify the
ABR waves. Study showed that the instantaneous
energy of auditory brainstem response can be used
as a marker to identify the ABR waves. This study
had proposed a platform for fast hearing screening
Keywords: Auditory Brainstem Response; Neonate;
Hearing Screening; Instantaneous Energy
1.1. Newborn Hearing Screening
Hearing screening of neonates is the key to prevent the
most serious consequences of hearing loss. One of the
most common neurosensory handicaps in newborns and
children is congenital hearing loss [1,2]. Hearing plays a
basic role in speech, intellectual and language develop-
ment. The importance of early detection and rehabili-
tation of infants with hearing impairment cannot be
overstated. Unfortunately, the average time between
birth and the detection of congenital sensorineural (SN)
hearing loss is 2.5 years. The American Joint Committee
on Infant Hearing recommended that audio logical reha-
bilitation should begin within the first 6 months of life
(3). In cases with pronounced hearing loss even no
speech ability will be developed at all causing serious
communication problems and impaired intellectual and
emotional development. The consequences of being
deaf-mute are the needs of special schools and care,
social isolation and no development of potential skills.
Thus there are serious medical and economical conse-
quences for the entire society due to this problem [1].
According to the World Health Organization, 5 per
1,000 neonates are born with significant hearing loss.
Data on prevalence of congenital Permanent Childhood
Hearing Impairment (PCHI) differs from country to
country. The prevalence of PCHI has been estimated to
be 1.1-1.5 for every 1000 live births in Estonia, 1 in 900
in the UK to 1 in 2500 newborns in Atlanta, Georgia.
The prevalence of Hearing loss is variable among
different races, birth weight and other pregnancy risk
factors. It is 10-20 times higher in high risk babies as
compared to normal babies.
In Malaysia, there is no published data on the actual
prevalence of hearing impairment in children. Estimated
figures obtained from the Statistics Division, United
Nations Economic and Social Commission for Asia and
the Pacific (ESCAP) (2002) indicate that Malaysia with
a total population of 23.8 million and birth rate of 23.5
per 1000 population has about 559,000 babies born in a
year. If the prevalence of hearing loss of 1.5 in every
1000 live births is applied in our population, a rough
estimate is that about 840 babies with hearing loss are
born each year in this country [2]. However, one study
case have been done to determine the prevalence of
hearing loss among newborns delivered at Hospital
University Malaya shows that the prevalence is 0.42%
(16/3,762) [3].
All over the world, strategies used in UNHS are based
on otoacoustic emissions (OAE) and auditory brainstem
response (ABR). Particularly, methods based on ABR
are well-considered due to their high specificity and
A. Arooj et al. / J. Biomedical Science and Engineering 3 (2010) 816-821 817
Copyright © 2010 SciRes. JBiSE
sensitivity rates, and their high correlation between
impairment and alteration in the ABR pattern [4]. OAE
testing evaluates the integrity of the inner ear (cochlea).
In response to noise, vibrations of the hair cells in a
healthy inner ear generate electrical responses, known as
otoacoustic emissions. The absence of OAEs indicates
that the inner ear is not responding appropriately to
sound [5]. There are two types of OAE screening techni-
ques, Transient Evoked Otoacoustic Emissions (TEOAEs)
and Distortion Product Otoacoustic Emissions (DPOAEs)
The Auditory Brainstem Response (ABR) is one of
the most widely used auditory evoked potentials (AEP).
The ABR is a far-field, differentially averaged, electro-
physiologically recorded signal. It represents the sum-
med and averaged responses of thousands of nerve fibres
to repeated acoustic stimulation [7]. The stimulus is
delivered via earphones or an inserted ear probe and
scalp electrodes are used to pick up the signal. ABR
evaluates the integrity of the peripheral auditory system
and the auditory nerve pathways up to the brainstem and
is able to identify infants who have normal cochlear
function but abnormal eighth-nerve function (auditory
neuropathy) [5]. Detection of wave V in the ABR
measurements is the most reliable objective diagnosis
and quantification of hearing loss in children [8-11]. Due
to a poor signal-to-noise ratio, 2000-4000 sweeps have
to be averaged to obtain a meaningful, visually notice-
able signal at a particular stimulation level [12].
Usually in Malaysia, Hearing Screening is done when
babies are brought to the Maternal Child Health Clinics
or private clinics for their routine immunization using
the Infant Distraction Test or when children fail to attain
normal language milestones (personal communication).
Recently, newborn hearing screening has been intro-
duced in a few hospitals on a regular basis in the Klang
Valley. In other centers, babies with high risk factors
such as prolonged NICU stay, craniofacial anomalies or
previous history of meningitis are usually referred earlier
for a full audiological assessment. Typically, screening
programs use a 2-stage screening approach, either OAE
repeated twice, OAE followed by ABR, or ABR repeated
twice, or use a 3-stage screening approach, OAE repeat-
ed twice and followed by ABR [13,14]. NHS programs
are commonly conducted as multiple stage procedures.
Figure 1 shows the implementation of universal NHS in
the state of Saarland, Germany [6].
1.2. ABR Recording System
In 1984, an automated electric response audiometry
recording system has been developed [15]. It uses
Nascom 2, an 8 bit microcomputer with a Z80A pro-
cessor running at 4 MHz, 32 KB RAM and 8 input/
output ports, 12 bit analog to digital converter, low noise
differential amplifier, optically isolated amplifier and
DC to DC converter. An electro-sensitive printer has
been used to record the patient information and the ABR
result. The gain of the amplifier depends on the full-
scale voltage range of the A-D converter and mini- mum
voltage input requirements [16]. Typical gain values for
evoked response systems range from 10,000-500,000
[16-22]. Taking care of the gain of the amplifier is
important. It must be low enough to avoid saturation
Frequency bandwidth is important in order to get the
correct range for the signal. The frequency bandwidth of
the ABR signal, typically range from 20 Hz-5 kHz
[24-26]. This bandwidth will pass the wave v and slow
SN10 components of the ABR which are important in
the estimation of hearing thresholds. There are variety of
sampling rate value with variety of resolution bits that
has been used to sample the ABR signal; 12.8, 20 and 50
kHz; 12 and 16 bit [19,20,26].
1.3. Wave V Detection
Various kinds of methods have been introduced for
detection of ABR waves. Woodworth et al. [9],
introduced a matched filter technique, derived from an
ABR obtained at high stimulus level in order to calculate
wave V latency at lower intensity level as shown in
Figure 2. Strauss et al. [12], introduced an approach for
detection of ABRs using a smart single sweep analysis
system. The method used a small number of sweeps
which is decomposed by optimized tight frames and
evaluated by a kernel based novelty detection machine
[12]. Delgado and Ozdamar [18], mentioned that results
of spectral analysis, spectral filtering and fiber-tract
modeling of ABRs were used to determine the most
suitable filters to detect the position of the various peaks.
These analyses revealed general trends in ABR
Figure 1 Organization of 3 stage universal NHS Program
(implemented in the state of Saarland, Germany)
818 A. Arooj et al. / J. Biomedical Science and Engineering 3 (2010) 816-821
Copyright © 2010 SciRes. JBiSE
Figure 2 Detection of wave V in ABR.
composition from one intensity to another and were used
to write labeling rules [18]. Wilson and Aghdasi [15],
used a Discrete Wavelet Transform (DWT) of ABR
signals in order to detect the ABR waves. The wavelets
technique is used to decompose a signal into discrete
sets of details (high frequencies) and approximations
(low frequencies). The different scaled signals are then
rebuilt from their resulting wavelet coefficients and
analyzed in a method similar to the full signal analysis
[24]. In this study, the instantaneous energy of ABR
signal has been introduced as a marker to identify the
ABR waves. Instantaneous energy technique has pre-
viously been employed in other applications of ECG and
heart sound signal processing, such as heart sound seg-
mentation [27,28].
2.1. Experiment Setup
Figure 3 shows the diagram of the hardware system
setup. It consists of (A) gTec USBamp, a biosignal
amplifier and data acquisition machine, (B) gPAH, a
programmable attenuator, (C) trigger box, (D) head-
phone, (E) laptop, (F) MP3 player and (G) electrodes.
The gTec USBamp was used to amplify and acquire the
ABR signals. The gPAH was used to attenuate the click
stimulus produced by MP3 player. MP3 player could be
replaced by a laptop as well. The study was done on
infants at Pusat Kesihatan (Health Clinic) UTM, JB,
Malaysia. The infants were included in the study after a
written consent signed by their parents. Our Team
included biomedical engineers, nurses and medical doc-
tor. Under supervision of medical doctor, the electrodes
were applied by nurses on the infant’s frontal parietal
(FP) area and mastoid process. Trigger box was used to
produce trigger signal from the click stimulus where as
different laptops were used in the experiments in order
to get smooth clicks. The triggered signal was used to
segment the ABR signals. The recorded signals were
transferred to a laptop by gTec USBamp via USB port.
The recorded signal was filtered at bandwidth ranges
from 100 Hz-3000 kHz. The click stimulus rate was
setup at 10 clicks sec–1 and the signal was sampled at
19.2 kHz with 24 bit resolution. The stimulus intensity
levels used in the experiments are 80, 70 and 60 dBnHL.
The signals were averaged after 2048 click stimulus
repetitions. Figure 4 shows the electrode’s configuration
used in the experiments. The positive electrode (channel
1) was connected to FP2, the negative electrode (re-
ference) was connected to mastoid and ground electrode
was connected to forehead. The FP2 IS 10% from nasion
on the right side of parietal bone.
Matlab R2006a simulink software had been used to
capture the raw ABR signals from the gTec USBamp
and analyze the signals. However, few configurations
need to be carried out using the Matlab model. The
analysis algorithm was written in Matlab M-file format.
Figure 5 shows the presentation of the ABR averaged
signal and the instantaneous energy of the averaged
signal. Figure 5(a) shows the result acquired from a
normal person and Figure 5(b) shows the result acquired
from a hearing loss person. Both signals are recorded
with the intensity of 80 dB. On the ABR averaging
signal graph shows three different averaged signals.
The signals are plotted on three different baselines, 0, 1
Figure 3. Diagram of the hardware system setup.
Figure 4. Electrodes configuration.
A. Arooj et al. / J. Biomedical Science and Engineering 3 (2010) 816-821 819
Copyright © 2010 SciRes. JBiSE
and 2 uV respectively. The averaged signal on the 0 base-
line used is 500 sweeps, the averaged signal on the 1
baseline used is 250 sweeps, on the 2 baseline used is 125.
The dotted vertical line marked the latency of wave V. It
can be observed that wave V occurred at specific points in
the signal of normal person, Figure 5(a), but did not occur in
the signal of hearing loss person, Figure 5(b). Table 1
shows the summary of the graph. The normal subject
showed results with the latency 5.313 m/sec and amp-
litude range from 0.335-0.235 uV. The hearing loss
subject showed result with the latency 5.156msec and
amplitude range from –0.9288 to –0.707 uV.
The objective of the study is, to investigate the effect-
tiveness of instantaneous energy for the detection of
Figure 5. Wave V detection using instantaneous energy of ABR signal on (a) normal subject (b) hearing loss subject
820 A. Arooj et al. / J. Biomedical Science and Engineering 3 (2010) 816-821
Copyright © 2010 SciRes. JBiSE
Table 1. Summary of Figure 5.
Subject Intensity (dB) Latency (ms) 500 sweeps (uV) 250 sweeps (uV) 125 sweeps (uV)
Normal 80 5.313 0.3669 – 0 = 0.3669 1.235 – 1= 0.235 2.335 – 2 = 0.335
Hearing loss 80 5.156 –0.2991 – 0 = –0.2991 0.007111 – 1= –0.99288 1.293 – 2= –0.707
Table 2. Mean, standard deviation, and variance of the Averaging signal and its Instantaneous Energy.
No. of sweeps
IE Avg
Mean(msec) Standard
deviation Variance Mean(msec) Standard
deviation Variance
1000 5.23008 0.40246 0.16197 5.19092 0.40615 0.16496
800 5.23442 0.41394 0.17135 5.19967 0.40035 0.16028
625 5.25175 0.36077 0.13015 5.18658 0.39949 0.15959
500 5.25608 0.3508 0.12306 5.22575 0.38868 0.15107
250 5.28217 0.55715 0.31042 5.26908 0.40694 0.1656
125 5.22125 0.42306 0.17898 5.16483 0.37817 0.14301
100 5.1475 0.4778 0.2283 5.02608 0.49029 0.24039
80 5.1735 0.44885 0.20146 5.05208 0.45242 0.20468
50 5.20817 0.40954 0.16772 5.04342 0.41723 0.17408
40 5.23008 0.43164 0.18631 5.19092 0.46978 0.22069
20 5.25175 0.49195 0.24202 5.21267 0.4842 0.23445
10 5.20408 0.44679 0.19962 5.17367 0.43821 0.19203
ABR waves. ABR is an important clinical tool in the
identification and quantification of hearing impairment.
It is objective, noninvasive and unaffected by sleep or
drugs. Different signal detection techniques have been
developed and evaluated to improve test efficiency and
reliability [18]. The detection of responses at threshold
levels is not trivial and requires an experienced
professional. The amplitudes, a, in the Table 1 are
obtained by deduction of amplitude appeared on the
graph from the baseline. Thus, a, can be written as
a = b – c (1)
a = The real amplitude
b = The graph amplitude
c = The baseline
The latency of the signals is obtained by considering
the intensity of the click and the peak of instantaneous
energy of the signal which is near the latency value that
has been referred to the latency curve published by
Woodworth et al. [19]. For the intensity of 80 dB, the
latency curve shows that the latency should be within 5
m sec. In Figure 5(a), the peak of the instantaneous
energy is 0.2012 which is near the latency within 5 m
sec. In Figure 5(b), the peak of the instantaneous energy
that near the latency within 5 ms is 0.5402.Instantaneous
energy can detect wave ‘V’ even at lower sweeps while
some other techniques require higher number of sweeps.
Table 2 is showing mean, standard deviation and
variance of averaged signal and instantaneous energy on
time domain. From the table it’s obvious that use of
instantaneous energy is equally effective in capturing
wave ‘V’ of ABR. So it can be concluded that this is a
comparably effective technique. However, the limitation
is noise disturbance which interferes with detection of
ABR waves. A few false negative results were also noted
but hopefully in future there will be improvement in
technology and these problems will be overcome.
A new method of ABR wave detection has been
designed. The new method of the ABR wave’s detection
such as described in this study is important in order to
detect the hearing loss faster especially for infants and
children. The results have shown in this study states that
instantaneous energy of ABR signal can be used as
marker in order to detect ABR waves. The performance
of this method needs to be tested further.
This research project is supported by CBE (Center for Biomedical
Engineering) at University Technology Malaysia and funded by
University Technology Malaysia (UTM), Malaysia under grant
A. Arooj et al. / J. Biomedical Science and Engineering 3 (2010) 816-821 821
Copyright © 2010 SciRes. JBiSE
“Universal Hearing Screening in Malaysia Based On A Cost Efficient
Organization Structure Using An Innovative ABR Technology: The
Johor Screening Scheme” Vot 77013.
[1] Yoshinaga-Itano, C. (1999) Benefits of early intervention
for children with hearing loss. Otolaryngol. Clinics of
North America, 32(6), 1089-1102.
[2] Sivalal, S. (2005) Screening for hearing loss in infants.
Health Technology Assessment Report. http://www.moh.
[3] Joint Committee on Infant Hearing (1982) Position
statement. American Academy of Paediatrics. Pediatrics,
70, 496-497.
[4] American Academy of Pediatric (1999) Newborn and
infant hearing loss: Detection and intervention. Pe-
diatrics, 103, 527-530.
[5] Helfand, O.M., Thompson, D.C., Davis, R., McPhillips,
H., Homer, C.J. and Lieu, T.L. (2001) Newborn hearing
screening: Systematic evidence review. AHRQ Pub-
lication No. 02-S001, Agency for Healthcare Research
and Quality, Rockville.
[6] Delb, W., Merkel, D., Pilorget, K., Schmitt, J. and
Plinkert, P.K. (2004) Effectiveness of a teoae-based
screening program. Can a patient-tracking system effect-
tively be organized using modern information technology
and central data management? European Archives of
Otorhinolaryngology, 261(4), 191-196.
[7] Hall III, J. (1992) Handbook of auditory evoked
responses. Allyn and Bacon, Needham Heights.
[8] Wicke, J.D., Goff, W.R., Wallace, J.D. and Allison, T.
(1978) On-line statistical detection of average evoked
potentials: Application to evoked response audiometry
(ERA). Electroencephalography and Clinical Neuro-
physiology, 44(3), 328-343.
[9] Woodworth, W., Reisman, S. and Fointaine, A.B. (1983)
The detection of auditory evoked responses using a
matched filter. IEEE Transactions on Biomedical Engi-
neering, 30(7), 369-376.
[10] Mason, S.M. and Adams, W. (1984) An automated
microcosmputer based electric response audiometry
system for machine scoring of auditory potentials.
Clinical Physics and Physiological Measurement, 5(3),
[11] Shangkai, G. and Loew, M.H. (1986) An autoregressive
model of the BAEP signal for hearing threshold testing.
IEEE Transactions on Biomedical Engineering, 33(6),
[12] Strauss, D.J., Delb, W., Plinkert, P.K. and Schmidt, H.
(2004) Fast detection of wave V in ABRs using a smart
single sweep analysis system. Proceedings of the 26th
Annual International Conference of the IEEE Engi-
neering in Medicine and Biology Society, IEEE Xplore
Press, 1, 458-461.
[13] Abdullah, A., Hazim, M.Y.S., Almyzan, A., Jamilah, A.G.
and Roslin, S., et al. (2006) Newborn hearing screening:
Experience in a Malaysian hospital. Singapore Medical
Journal, 47(1), 60-64.
[14] Clemens, C.J., Davis, S.A. and Bailey, A.R. (2000) The
false-positive in universal newborn hearing screening.
Pediatrics, 106(1), e7.
[15] Wilson, W.J. and Aghdasi, F. (1999) Fast Fourier
transform analysis of the auditory brainstem response:
Effects of stimulus intensity and subject age, gender and
test ear. AFRICON, IEEE, 1, 285-290.
[16] American Speech-Language-Hearing Association (1987)
Short latency auditory evoked potentials. ASHA Practice
[17] Neely, S. and Liu, Z. (1998) ABRAV: Auditory brainstem
response average. http://www.boystownhospital.org/Media/
[18] Delgado, R.E. and Ozdamar, O. (1994) Automated
auditory brainstem response interpretation. IEEE Engi-
neering in Medicine and Biology Magazine, 13(2), 227-
[19] Dajani, H.R., Purcell, D., Wong, W., Kunov, H. and
Picton, T.W. (2005) Recording human evoked potentials
that follow the pitch contour of a natural vowel. IEEE
Transactions on Biomedical Engineering, 52(9), 1614-
[20] Boston, J.R. (1981) Spectra of auditory brainstem
responses and spontaneous EEG. IEEE Transactions on
Biomedical Engineering, 28(4), 334-341.
[21] Wilson, W.J. and Aghdasi, F. (2001) The importance of
pre-analysis windowing on auditory brainstem response
fast Fourier transform analysis. Scandinavian Audiology,
30(1), 3-12.
[22] Wilson, W.J. (2004) The relationship between the
auditory brain-stem response and its reconstructed
waveforms following discrete wavelet transformation.
Clinical Neurophysiology, 115(5), 1129-1139.
[23] Bell, S.L., Smith, D.C., Allen, R. and Lutman, M.E. (2004)
Recording the middle latency response of the auditory
evoked potential as a measure of depth of anaesthesia.
British Journal of Anaesthesia, 92(3), 442-445.
[24] Wilson, W.J. and Aghdasi, F. (1999) Discrete wavelet
analysis of the auditory brainstem response: Effects of
subject age, gender and test ear. AFRICON, IEEE, 1,
[25] Beltran, N. and Cornejo, J.M. (2003) Simultaneous
transient evoked otoacoustic emissions and auditory
brainstem response for auditory evaluation. Proceedings
of the 25th Annual International Conference of the IEEE
Engineering in Medicine and Biology Society. IEEE
Xplore Press, 4, 3222-3224;
[26] Strauss, D.J., Delb, W. and Plinkert, P.K. (2004)
Objective detection of the central auditory processing
disorder: A new machine learning approach. IEEE Tran-
sactions on Biomedical Engineering, 51(7), 1147- 1155.
[27] Malarvili, M.B., Kamarulafizam, I., Hussain, S. and
Helmi, D. (2003) Heart sound segmentation algorithm
based on instantaneous energy of electrocardiogram.
Computers in Cardiology, 30, 327-330.
[28] Rushaidin, M.M., Salleh, S.-H., Swee, T.T., Najeb, J.M.
and Arooj, A. (2009) Wave V detection using instan-
taneous energy of auditory brainstem response signal.
American Journal of Applied Sciences, 6(9), 1669-1674,