PRELIMINARY RESULTS OF CLINICAL TESTS OF A NEW

NEURAL-NETWORK-BASED OTITIS MEDIA

ANALYSIS SYSTEM

M. Hannula

, T. Holma

, E. Löfgren

, H. Hinkula

and M. Sorri

Medical Engineering R&D Center, Oulu University of Applied Sciences, Kotkantie 1, 90250, Oulu, Finland

Department of Clinical Medicine/Otorhinolaryngology, University of Oulu, Oulu, Finland

Keywords: Acute otitis media, Middle ear, Artificial neural networks.

Abstract: Evaluation of middle ear effusion is essential in diagnostics of otitis media. In this study a new otitis media

diagnostic system based on acoustic reflectometry (AR) was preliminarily evaluated and tested with

experimental clinical data on 114 ears of 57 children. In the study the ears of the children were measured

with the new AR system and the corresponding ear status was definitively assessed in myringotomy by

measuring the amount of effusion in the middle ear. The collected data included successful measurements of

71 normal ears (no effusion in the middle ear) and 43 ears with 0.02-0.37 g of middle ear effusion. In the

analysis the correspondence between a neural network analysis of the AR measurement data and the

corresponding amount of middle ear effusion was analysed using a leave-one-out validation procedure. The

preliminary results were promising; the neural network analysis result and the amount of middle ear

effusion correlated statistically significantly (p < 0.001), with correlation coefficient R = 0.37. In future

studies more data will be collected to obtain higher correlation in the analysis.

1 INTRODUCTION

Otitis media is one of the most common reasons for

medical contacts in children. Many studies (Linden

et al., 2006; Chianese et al., 2007; Walsh et al.,

1998; Block et al. 1999) have evaluated acoustic

reflectometry (AR) -based (Teele and Teele, 1984)

diagnostics of otitis media with good results. The

idea of the new neural-network-based (Haykin,

1999) AR system (Hannula et al., 2009) was to build

an Internet-integrated AR measurement and analysis

system which includes neural-network-based

analysis, having the capability to incrementally

upgrade its performance due to an increasing amount

of data in its database. In this study this system was

preliminarily evaluated with a small amount of

clinical data.

In this study children were examined with the

new system and their ear status was determined in

the Department of Otorhinolaryngology of Oulu

University Hospital. The performance of the neural-

network-based AR measurement analysis was

assessed with a leave-one-out validation procedure

(Haykin, 1999), and the correlation between the

neural network result and the measured effusion

status was evaluated.

2 METHODS

Fig. 1 illustrates the measurement system. In the

measurement process, first the measurement tip is

connected to a PC’s sound card. Next the

measurement software is started from a web page.

Next the ears of the subject are measured. The

resulting measurement data are then sent to an

artificial neural network located on a web server,

which predicts the corresponding ear status from the

measurement data with the help of a simple index

value. The result is shown to the user on the web

page. In this study, after the AR measurement the

status of the ears was clinically determined in

myringotomy, where the amount of middle ear

effusion was measured.

365

Hannula M., Holma T., Löfgren E., Hinkula H. and Sorri M..

PRELIMINARY RESULTS OF CLINICAL TESTS OF A NEW NEURAL-NETWORK-BASED OTITIS MEDIA ANALYSIS SYSTEM.

DOI: 10.5220/0003679703650368

In Proceedings of the International Conference on Neural Computation Theory and Applications (NCTA-2011), pages 365-368

ISBN: 978-989-8425-84-3

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

Figure 1: The measurement system.

The AR measurement was done following the

conventional measurement protocol (Linden et al.,

2006; Chianese et al., 2007; Walsh et al. 1998;

Block et al., 1999). The frequency band of the

stimulus signal in the measurement was 0.5-5 kHz.

In this study the data were analysed in the frequency

band of 1-3.5 kHz, which according to the AR

measurement principle (Teele and Teele, 1984),

should include the most essential information on the

otitis media -related status of the ear.

3 SUBJECTS AND DATA

The study was prepared following the standard

clinical research protocol, and the study was

approved by the Ethics Committee of the Northern

Ostrobothnia Hospital District. The data set of this

study originally included 133 ears of 73 children

(age 0.5-5 years, median weight 12.4 kg, min 7.8 kg,

max 44 kg) who were measured with the AR system

and clinically examined. Due to the preliminary

characteristics of the very first clinical

measurements, part of the data was unusable

because of a substantial amount of noise in the

measured signal or missing information on the

weight of the middle ear effusion. Therefore, the

total number of children evaluated successfully in

this study was 57, with 114 ears. The characteristics

of the data are shown in Table 1.

Table 1: Characteristics of the original data.

Ear status N (total) N (successful)

Normal 85 71

With effusion 48 43

Total 133 114

The minimum amount of effusion in the ear in ears

with effusion status was 0.02 g, and the maximum

amount was 0.37 g (median 0.18 g). To simplify

calculations, the amount of effusion was expressed

in this study by the equation:

weighteffusion

index

37.0

1−= (1)

where effusion_weight is the amount of effusion in

the ear; if the ear was healthy, the value was

determined to be zero. Therefore, a healthy ear

(effusion_weight = 0) was indicated with an index of

1 and an ear with maximum amount of effusion

occurring in the present dataset (effusion_weight =

0.37 g) was indicated with an index of 0. Fig. 2

shows the distribution of the ear statuses of the data

expressed with the index values.

Figure 2: Distribution of ear status indexes.

4 METHODS

In the analysis a generalized-regression-based neural

network (Haykin, 1999; Wasserman, 1993) was

trained to estimate the index value indicating the

amount of effusion in the ear on the basis of AR

measurement data, Fig. 3. The performance of the

neural network was evaluated with a leave-one-out

validation procedure (Haykin, 1999), where the

network was trained 114 times (total number of data

sets). During each training period one measurement

of the whole data set was excluded from the training

data set and used in the validation phase.

NCTA 2011 - International Conference on Neural Computation Theory and Applications

366

Figure 3: Neural network structure.

5 RESULTS

The neural network training and validation were

done with Matlab® software (Mathworks Inc,

Natick, USA). The correlation between the neural

network output and the amount of effusion as index

values in the ear is shown in Fig. 4.

Figure 4: Results of the neural network validation

(R=0.37, p < 0.001).

Fig. 4 shows that the neural network index and the

index of the measured amount of effusion correlate

statistically significantly (R = 0.37, p < 0.001).

6 DISCUSSION

AND CONCLUSIONS

The validation of the neural network analysis shows

that the correlation between the neural network

output and the measured amount of effusion in the

ear is clear, even though the correlation value is not

very high due to the preliminary characteristics of

the study. The result is in line with previous studies

(Linden et al., 2006; Chianese et al., 2007; Walsh et

al., 1998; Block et al., 1999; Teele and Teele, 1984)

and illustrates that the essential characteristic

features needed to estimate the amount of effusion in

the ear can be found from the AR measurement data.

In future studies the amount of data will be

increased in order to get higher correlations with

smaller deviations in single measurements. The idea

in this further development is to iteratively increase

the training data of the neural network of the

analysis system.

The data used in this investigation included a

number of unsuccessful measurements, 29 ears in

total. Technical problems which occurred during the

preliminary data collection were carefully

investigated and related improvements to the data

collection procedure were implemented in

subsequent measurements.

It should be kept in mind that in this study the

output of the network was an index with a linear

relationship to the amount of effusion only. Hence,

for example the pressure status of the ear was not

taken into account, which may account for part of

the deviations between the network output and the

determined ear status in the case of healthy ears with

no effusion at all. In order to improve the accuracy

of the measurements, further studies will more

extensively take into account the features of the ear,

related anthropometric characteristics and the middle

ear pressure. Also, the simplified linear index-based

approach used in this study may decrease the

correlation value between the neural network output

and the measured amount of effusion; application of

an appropriate nonlinear index would be better.

To conclude, the preliminary results of this

study were encouraging. The applied neural network

method has the capability to estimate the amount of

effusion in the ear at a statistical level, as shown

with the data set presented in this study. After

collection of the data set published in this study, the

number of data sets was increased essentially in the

next phase of the research project. In this phase an

improved measurement procedure was applied,

which substantially increased the repeatability and

noise tolerance of the measurements. The results of

those measurements will be published soon,

including evaluations about sensitivity and speficity

of the system in diagnostic application. Further, an

especially interesting application of the presented

measurement system is the possibility to use it at

PRELIMINARY RESULTS OF CLINICAL TESTS OF A NEW NEURAL-NETWORK-BASED OTITIS MEDIA

ANALYSIS SYSTEM

367

home as a tool to improve otitis media treatment

processes in health care.

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