SELECTION OF AN ARTIFICIAL NEURAL NETWORK MODEL

TO DIAGNOSIS MOUTH-BREATHING CHILDREN

Felipe Mancini, Ivan Torres Pisa

Health Informatics Department, Federal University of São Paulol, Rua Botucatu 862, São Paulo, Brazil

Liu Chiao Yi, Shirley Shizue Nagata Pignatari

Pediatric Otorhinolaryngology Department, Federal University of São Paulol, São Paulo, Brazil

Keywords: Artificial neural networks, posture, mouth breathing, clinical decision support systems.

Abstract: A number of factors can lead to changes in body posture, basically determined by alterations in the natural

curvature of the spine. Such changes, in turn, may also result in secondary health problems. Mouth

breathing is thought to be one of these problems. Experiments with healthy nasal breathing individuals have

showed that when they are forced to breathe through their mouth only the natural shape of their spine curves

change. However the characterization of the spine curvature in mouth breathers has not been done yet and

the matter lies on the personal experience of the health professional. This study reports on the preliminary

findings of a broader research which attempts to characterize the changes in the behaviour of the spine,

caused by mouth breathing, by using artificial neural network modelling and data from 52 subjects. Four

different models – backprogation, learning vector quantization (LVQ), and self-organizing map (SOM) –

were tested for best performances in sensitivity and specificity in diagnosing mouth and nasal breathing

children. Competitive-learning-based algorithms – LVQ and SOM – presented the best performance for

current data set.

1 INTRODUCTION

Breathing is the first vital function developed at

birth becoming the main body function, and as such

should be cared for. Chronic mouth breathing is

associated with pediatric, allergy-related and

otorhinological complaints.

The narrowing of the pharynx has been reported

to be associated with forward extension of the neck

in the attempt to straighten the pharyngeal tube in

order to improve the reduced air flow through it

(Solow, 1984).

The skull, mandible, cervical portion of the

spine, and upper airways can be viewed as a system

in which the positioning of its parts are closely

related. Mouth breathing, a physiological change in

the correct respiratory process, determines postural

changes due to the altered, interrelated performance

of the muscles in each of the parts that integrates the

above mentioned system (Rocabado, 1979; Ribeiro,

2003). Studies on body posture of mouth-breathing

children have reported as characteristic features of

these individuals: forward positioned head and

shoulders, lordosis, protruded scapulas, frontal

depression of the thorax, and protruded abdomen (

Aragão, 1991; Liu, 2003). However, no studies

further characterizing the posture of mouth breathers

are currently available.

A computer-aided tool could be useful for health

professionals if it could characterize the postural

changes caused by mouth breathing. Artificial neural

networks (ANN) (Haykin, 1999) have been used

successfully in treating and analyzing biomedical

data

. ANN can provide a faster data analysis when

associations between factors and outcomes are not

linear or when there are a great number of factors

(high dimensionality) to be analyzed

(Lisboa, 2002).

Furthermore, ANN can lessen the influence of

confounding variables (noise) (Reggia, 1993).

The aim of this study is to report the preliminary

findings regarding the selection of an ANN model

for identifying mouth-breathing children through the

analysis of their posture.

197

Mancini F., Torres Pisa I., Chiao Yi L. and Shizue Nagata Pignatari S. (2008).

SELECTION OF AN ARTIFICIAL NEURAL NETWORK MODEL TO DIAGNOSIS MOUTH-BREATHING CHILDREN.

In Proceedings of the First International Conference on Health Informatics, pages 197-200

 SciTePress

2 MATERIALS AND METHODS

The data used in this study was collected at Imaging

Department and Pediatric Otorhinolaryngology

Department at Federal University of São Paulo

(UNIFESP), Brazil. Fifty two children were assessed

including 30 previously diagnosed as mouth-

breathing subjects and 22 nasal breathers. The

variables collected for analysis are shown in Table 1.

Table 1: Study variables.

Anthropometrics Diaphragm Posture

Sex

Cervical

Curvature

Age

Right side

Excursion (PD)

Lumbar

Curvature

Weight

Thoracic

Curvature

Height

Left side

Excursion (PE)

Pelvis Positioning

The imaging of diaphragm excursion was

obtained by videofluoroscopy and recorded for

analysis using Adobe Photoshop

(Adobe Systems

Inc.) software. Due to the limited size of the

fluoroscope screen, the imaging of the left and right

sides of the diaphragm was recorded separately. The

posture of the participating children was assessed

using photographs of the subjects’ left-side view on

which angles formed by key body features were

analyzed using a specially developed software

(Software for Posture Evaluation, SAPO) (Duarte,

2006). Figure 1 shows these key features and the

angles they determined.

Figure 1: Representation of key points, and respective

angles they formed, used in the posture assessment: (a)

cervical curvature; (b) thoracic curvature; (c) lumbar

curvature; (d) pelvis positioning.

The data collected were used to determine the

ANN model that showed the best performance

among a number of models which included,

backpropagation (BP) (Haykin, 1999), learning

vector quantization (LVQ) (Kohonen, 1997), and

self-organizing map (SOM) (Kohonen, 1997). Such

a comparison was carried out through Matlab

development tool (The MathWorks Inc., Natick,

MA, USA) and implementation packages SOM

Toolbox package (Vesanto, 2000) for SOM and

LVQ models and the Neural Networks Toolbox

(The MathWorks Inc., Natick, MA, USA) for BP

model.

The structure of each ANN model was as

follows:

 Backpropagation - Network structure: 20 nodes

in the first hidden layer, 5 nodes in the second

hidden layer and 1 node in output layer.

Training function: Levenberg-Marquardt.

Maximum number of epochs to train: 100.

Minimum performance gradient: 10

-10

;

 LVQ - Network structure: 3 x 3 nodes. Vectors

prototypes initialization: linear.

Neighbourhood relationship: hexagonal.

Running length: 100. Learning rate used in

training: 0.001;

 SOM - Network structure: 3 x 3 nodes. Vectors

prototypes initialization: linear.

Neighbourhood relationship: hexagonal.

Neighbourhood function: gaussian.

The performances of these ANN models were

measured by determining the rates for sensitivity and

specificity obtained with each model, when carrying

out the leave-one-out cross-validation (Burnham,

2004). Receiver Operating Characteristic (ROC)

(Metz, 1978) curve analysis was used to determine

the association ANN model-input pattern with the

best performance among those showing higher rates

of sensitivity and specificity.

Some investigation of factors potentially

associated with the shaping of spine curves – body

weight, height and excursion of the diaphragm – was

also carried out by inputting them either separately

or in combination with the data collected regarding

spine curvature (Table 2).

Table 2: Different input patterns used to determine the

influence of potential influencing factor on spine

curvature. The number between brackets indicates the

number of variables of a same subset.

Input Pattern (IP) IP Label

spine curvatures (4)

and diaphragm excursion (2)

IP 1

spine curvatures (4), diaphragm excursion

(2), weight (1) and height (1)

IP 2

spine curvatures (4) IP 3

diaphragm excursion (2) IP 4

weight (1) and height (1) IP 5

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3 RESULTS

The sensitivity and specificity rates attained with

each of the study input patterns and each of the

study ANN model varied from 0.76 to 1 and from

0.57 to 0.99 respectively (Table 3).

The areas under the ROC curves for LVQ and

SOM inputting IP3 were 0.94 and 0.90, respectively,

for SOM inputting IP2 was 0.92 and inputting IP1

0.97.

Table 3: Specificity (sp) and sensibility (se) values

calculated through leave-one-out algorithm for different

data sets and for all ANN models analyzed in this study.

RNA

Models

SOM LVQ BP

Input

Pattern

sp se sp se sp se

PE1 0.97 0.97 0.99 0.90 0.98 0.94

PE2 0.98 0.95 0.96 0.96 0.86 0.94

PE3 0.98 0.93 0.98 0.97 0.96 0.90

PE4 0.88 0.87 0.88 0.90 0.88 0.90

PE5 0.80 0.76 0.67 0.76 0.67 0.76

4 DISCUSSION

The area under the ROC curve for IP2 (all variables

studied) inputted in SOM (0.92) indicates that this

input pattern improves the performance of SOM but

is still bellow the performance of LVQ using a much

simpler set of input variables (IP3).

However, a previous statistical test not shown in

this study (t-Student test) presented a statistically

significant association between the data collected on

the excursion of the diaphragm and the child being a

mouth breather. This association seems to reflect on

the area under the ROC curve (0.97) calculated for

SOM model when the variables associated with

spine curvature and diaphragm excursion (IP1) was

inputted.

Despite the input of the data referring to the

diaphragm excursion (IP1 and IP2) yielding a better

performance of SOM, the fluoroscopic investigation

is an additional medical examination that is not

usually performed in the clinical practice. Therefore,

if we are to deal with such limitation, LVQ model

associated with the input of variables of spine

curvature only (IP3) can presently be a good

alternative model due to its high rates of sensitivity

and specificity.

Including the variables weight and height to the

set spine curvature and diaphragm excursion (IP1) to

form IP2 resulted in lower performance of SOM

model according to ROC curve analysis. This agrees

with previous statistical analysis (Student’s t-test)

showing that the variation of weight and height

between mouth and nasal breathers was not

statistically significant.

Pesonen et al. (1996), Markeya et al. (2003), and

Ng & Chong (2006) compared the performance of

SOM and BP models in different tasks of

classification of biomedical data and found that BP

had higher rates of specificity and sensitivity. This

was not the case in the present study. In fact,

training in SOM is unsupervised, which would

support its worse performance in data classification

as compared with models using supervised training.

A potential explanation for the best performance of

SOM over BP in the present study is the limited set

of data (52 patients) for training and validation

currently available.

As previously mentioned, the present report is

part of a broader biomedical study. So far, the use of

computer-aided modelling focused the development

of a reliable diagnosis tool. This is deemed to be the

first step to develop a second and perhaps more

important tool that could indicate the severity of

changes in body posture and assist the decision

making regarding the prescription of a

physiotherapeutic treatment for such condition.

ANN modelling is a resource that could overcome

the complexity of such task.

5 CONCLUSIONS

The best rates of sensitivity and specificity were

attained for variables associated with the spine

curvature only (IP3) inputted in LVQ model. A

further comparison of performance using IP3 was

carried out between SOM and LVQ models using

their respective ROC curves which showed that the

area under the curve for LVQ model was larger

(0.94) than that for SOM (0.90).

Although supervised learning ANN models, such

as BP model, have been reported to yield better rates

of sensitivity and specificity, the present study found

that SOM and LVQ, both competitive-learning-

based algorithms, had better performance.

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CHILDREN

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REFERENCES

Aragão W. Aragao´s function regulator, the

stomatognathic system. J Clin Ped Dent. 1991;

15(4):226-231.

Burnham, KP, Anderson D. Model Selection and Multi-

Model Inference. Berlim: Springer; 2004.

Duarte M. Software for Posture Evaluation (SAPO),

Brazil: University of São Paulo; 2006.

Haykin S. Neural Networks: a Comprehensive

Foundation. New Jersey: Prentice-Hall; 1999.

Kohonen T. Self-organizing Maps. Berlim: Springer-

Verlag; 1997.

Lisboa PJG. A Review of Evidence of Health Benefit from

Artificial Neural Networks in Medical Intervention.

Neural Netw. 2002; 15: 11-39.

Liu CY, Guedes ZCF, Pignatari SSN, Weckx LLM.

Alteração da postura corporal em crianças

respiradoras bucais. Fisioter Mov. 2003;16(3):29-33.

Markeya MK, Lo JY, Tourassib GD, Floyd CE, Self-

organizing map for cluster analysis of a breast cancer

database, Artif Intell Med, 2003, 27, 113–127.

Metz CE. Basic principles of ROC analysis. Seminars in

Nucl Med. 1978;8:283–98.

Ng EY & Chong C. ANN-based mapping of febrile

subjects in mass thermogram screening: facts and

myths. J Med Eng Technol. 2006; 30(5):330-7.

Pesonen E, Matti E, Juhola M. Comparasion of Different

Neural Network Algorithms in the Diagnosis of Acute

Appendicitis. Int J Biomed Comput. 1996; 40:227-233.

Reggia JA. Neural Computation in Medicine. Artif Intell

Med 1993; 5: 143-157.

Ribeiro EC, Soares LM. Avaliação espirométrica de

crianças portadoras de respiração bucal antes e após

intervenção fisioterapêutica. Fisioter Bras.

2003;4(3):163-7.

Rocabado M. Cabeza Y. Cuello – tratamiento articular.

Buenos Aires: Intermédica, 1979.

Solow B, Siersbaek-Nielsen S, Greve E. Airway adequacy,

head posture, and craniofacial morphology. Am J

Orthod. 1984;86:214-23.

Vesanto J, Himberg J, Alhoniemi E, Parhankangas J. SOM

toolbox for Matlab 5. Espoo: Helsinki University of

Technology; 2000.

HEALTHINF 2008 - International Conference on Health Informatics

200