NEWBORN HEARING SCREENER BASED ON AUTOMATIC

AUDITORY BRAINSTEM RESPONSE DETECTION

Julio Aldonate, Carlos Mercuri, José Biurrun, Juan Manuel Reta, Claudia Bonell

Gerardo G. Gentiletti, Sergio Escobar and Rubén Acevedo

Laboratorio de Ingeniería de Rehabilitación e Investigaciones Neurológicas y Sensoriales, Facultad de Ingeniería

Universidad Nacional de Entre Ríos, Ruta 11 - Km10, Oro Verde, Entre Ríos, Argentina

Keywords: Auditory brainstem response, hearing loss, automatic detection, newborn hearing screening.

Abstract: Hearing loss is one of the pathologies with the highest prevalence in newborns. If it is not detected in time,

it can affect the nervous system and cause problems in speech, language and cognitive development. The

recommended methods for early detection are based on otoacoustic emissions (OAE) and/or auditory

brainstem response (ABR). In this work, the design and implementation of an automated system based on

ABR to detect hearing loss in newborns is presented. Preliminary evaluation in adults was satisfactory.

1 INTRODUCTION

According to the Health World Organization, 5 per

1,000 neonates are born with significant hearing

loss. This pathology has a negative incidence on the

nervous system, causing a delay in the development

of speech and language, and consequently affecting

cognitive and behavioral skills (Priesler, 1999;

Jacobson, 1985).

Early detection of hearing impairment is

therefore essential, in order to allow a more

successful intervention and rehabilitation.

Recommended practice begins with universal

newborn hearing screening (UNHS) using objective

physiological methods, preferably either before

being discharged from the hospital or no later than

at 2 or 3 months of age (Gracey, 2003).

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 sensitivity rates and their high

correlation between impairment and alteration in the

ABR pattern (American Academy of Pediatric,

1999; European Consensus Statement on Neonatal

Hearing Screening, 1998).

However, traditional analysis is complex and

time consuming, limiting its use to selective

screening of a small percentage of newborns. With

the development of Automatic ABR (AABR)

analysis, results are obtained faster, facilitating its

application on a larger population (Suppiej &

Rizzardi, 2007).

In this context, the Facultad de Ingeniería de la

Universidad Nacional de Entre Ríos (Argentina), in

cooperation with the Hospital Materno Infantil San

Roque (HMISR), Paraná, Argentina, and the Centro

de Especialidades Médicas Ambulatorias (CEMA),

Rosario, Argentina, have begun a technological

development project, aiming at designing and

building a universal hearing screening system based

on AABR. This work presents the design and

preliminary results obtained with an implemented

prototype, named AudioScreen.

2 SYSTEM REQUIREMENTS

A universal hearing screener based on AABR should

be able to evoke, record, store and process

electroencephalographic (EEG) signals coming out

from the brain (McAllister & McCullagh, 2000).

Therefore, suitable hardware and/or software for

these purposes must be able to perform the

following tasks:

 Stimulate the auditory system according with

specific parameters (Mercuri et al., 2006).

 Record the EEG signals from the scalp,

synchronizing the acquisition with the

stimulation (Acevedo et al., 2006).

174

Aldonate J., Mercuri C., Biurrun J., Manuel Reta J., Bonell C., G. Gentiletti G., Escobar S. and Acevedo R. (2008).

NEWBORN HEARING SCREENER BASED ON AUTOMATIC AUDITORY BRAINSTEM RESPONSE DETECTION.

In Proceedings of the First International Conference on Biomedical Electronics and Devices, pages 174-177

DOI: 10.5220/0001052401740177

 SciTePress

 Perform a digital processing on the EEG

signals, in order to detect whether the ABR is

present or not (Acevedo et al. 2006).

The system has to be flexible enough to allow a

range of stimulation and recording options. Tables 1

and 2 summarize specific requirements for this

system.

Table 1: Stimulation parameters.

Parameter Description

Stimulus type Condensation click

Stimulus width 100 µs

Stimulus intensity

20 to 70 dB peSPL in 10 dB

steps

Stimulation frequency 11 and 31 clicks/s

Audio output Mono

Masking None

Table 2: Recording parameters.

Parameter Description

Gains 500 a 100.000 in 10 steps

Bandwidth 100 Hz – 2500 Hz

Input impedance

> 10

CMRR > 80 dB

Isolation > 3.000 V

RMS

Sampling frequency 200 Hz to 20 kHz

Sampling period Up to 15 ms

Resolution > 10 bits

3 SYSTEM DESIGN

The design of the hearing screener is based on four

modules: auditory stimulation (AS), signal

conditioning (SC), signal acquisition and

digitalization (SAD) and signal processing and

visualization (SPV). Figure 1 shows a block diagram

of these modules.

As this is the first research prototype, it is

planned to be used both in research and clinical

environments. The former requires this system to be

flexible at the digital processing stage, in order to

allow testing and evaluation of different algorithms

for automatic ABR detection. For this reason, the

SPV module is implemented in software, and a

personal computer is required in order to use the

screener. Future versions of AudioScreen will

implement this module in specific hardware, making

the system independent from the computer.

Figure 1: Block diagram of the AudioScreen hearing

screener modules.

A brief description of the system operation would be

as follows: initially, the computer sends a number of

configuration parameters for the AS and the SAD

modules. When a start signal is received, the

stimulator generates acoustic stimuli, which are

applied to the patient through headphones. At the

same time, a synchronism signal is generated and

sent to the SAD module to establish the beginning of

the EEG recording which is amplified and filtered in

the SC module. Finally, the EEG is digitalized and

transmitted to the computer via USB 1.0, where the

SPV module stores it for further processing and

visualization.

4 SYSTEM IMPLEMENTATION

4.1 Auditory Stimulation Module (AS)

The auditory stimulator produces digital clicks

(electrical stimuli) whose amplitude is set by a gain-

controlled amplifier (PGA2310 Texas Instruments

Inc). Then, the clicks are transduced to auditory

stimuli by headphones (Telephonics

TDH39). Two

stimulation parameters can be set: intensity, ranging

from 20 to 70 dB peSPL in 10 dB steps, and

frequency in two possible values, 11 or 33 clicks/s.

The selected values are indicated by LEDs on the

front panel. In this way, different configurations may

be used for auditory stimulation.

The electronic circuit is implemented with an 8-

bit RISC microcontroller. The microcontroller

configures the gain-controlled amplifier through a

serial port interface and it is linked up with the SAD

module using two ports: one is used to generate the

synchronism signal and the other is used to receive a

start signal to begin the stimulation.

NEWBORN HEARING SCREENER BASED ON AUTOMATIC AUDITORY BRAINSTEM RESPONSE DETECTION

175

4.2 Signal Conditioning Module (SC)

Appropriate integrated circuits (IC) for medical

instrumentation are used. This module consists of

four parts:

 Instrumentation amplifier: INA128 (Burr-

Brown Corp.). This IC has high input

impedance (10

Ω) and common-mode

rejection (120dB at G ≥ 100), very low offset

voltage (50µV) and drift (0.5µV/°C).

 Programmable gain amplifier: OP07 (Analog

Devices Inc.). Gain can be set in the range of

500 and 100000 in 10 steps.

 Isolating amplifier: ISO121 (Burr-Brown

Corp.). This IC has unitary gain and it is based

on a 2 pF differential capacitive barrier, which

results in a 3500 V

RMS

isolation.

 Filter: a band pass filter is implemented using

a cascade design of Sallen-Key cells with

cutoff frequencies in 2500 Hz for low pass

and 100 Hz for high pass. In order to reduce

the electromagnetic induction produced by the

power line, a Sallen-Key notch filter centered

in 50 Hz is implemented. This notch filter can

be enabled or disabled using onboard jumpers.

4.3 Signal Acquisition and

Digitalization Module (SAD)

This module is based on another 8-bit RISC

microcontroller which controls the AD module as

well as the communication with the computer. The

AD module includes a 12-bits, bipolar input range,

analogical to digital converter and a low-noise,

temperature-stabilized, precision reference. The

sampling frequency can be set to a maximum of 20

kHz and the acquisition window is set to 15 ms.

4.4 Power Supply

The power supply has two stages: one of them is not

isolated and the other is isolated according to the

IEC 60610 standard. Isolation is achieved using

PWR1303A (C&D Technologies Inc.). Table 3

summarizes its specifications.

Table 3: Power supply specifications.

Parameter Description

Isolated stage ± 5 V, 150 mA, 4 kV DC

Non-isolated stage ± 5 V, ± 12 V, 1 A

Hardware-implemented modules are shown in

Figure 2.

Figure 2: Inside view of the screener; (a) SAD module

board, (b) SC module board, (c) power supply board, and

(d) AS module board.

4.5 Signal Processing and Visualization

Module (SPV)

SPV module block diagram is shown in Figure 3.

This module was programmed using Borland

C++

Builder

, and the database was generated with

Microsoft

Access

2003. The user interface was

designed taking into account pieces of advice from

several professionals of the health centers mentioned

above.

EEG ACQUISITION EEG PROCESSING

DATABASE

ABR

VISUALITATION

REPORT

GENERATION

Figure 3: Block diagram of the SPV module.

The database stores all the information about the

screening, which basically consists of newborn’s and

mother’s personal information, along with the

signals recorded with the EEG acquisition block and

the parameters used in the study, e.g. stimulation

frequency and intensity.

The EEG processing block performs two main

tasks: signal averaging to enhance signal-to-noise

ratio, and automatic ABR detection using the FSP

algorithm (Gentiletti et al., 2003). The results of the

study are visualized in the computer monitor (Figure

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4), and can be printed along with a full report of the

study.

Figure 4: ABR visualization window.

5 EXPERIMENTS AND RESULTS

Figure 5 shows the complete system, which

complies with the IEC 60610 standard for medical

equipment.

Figure 5: AudioScreen system with the software running

on the computer; (a) screener; (b) software interface; (c)

electrode cable; (d) power supply.

The user interface was assessed by HMISR and

CEMA personnel, and the resulting evaluation was

satisfactory. Finally, a full set of tests were carried

out with healthy patients, which verified the correct

performance of the system.

6 CONCLUSIONS

A first prototype of the AudioScreen system was

designed, built and it’s has been tested. It was done

using adequate off the shelf electronic components,

which met the standard for medical equipment and

were fully operational. The following step is to

perform system validation at the HMISR as well as

the CEMA. In addition, an algorithm of ABR signal

detection based on support vector machines is being

developed, which will be validated and contrasted

against the Fsp method.

ACKNOWLEDGEMENTS

This work was supported by the Secretary of

Science, Technology and Productive Innovation

(SeCTIP) and by the Council of Investigations of the

National University of Entre Ríos (CIUNER).

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