Authors:
Felipe Mancini
;
Ivan Torres Pisa
;
Liu Chiao Yi
and
Shirley Shizue Nagata Pignatari
Affiliation:
Federal University of São Paulol, Brazil
Keyword(s):
Artificial neural networks, posture, mouth breathing, clinical decision support systems.
Related
Ontology
Subjects/Areas/Topics:
Biomedical Engineering
;
Decision Support Systems
;
Health Information 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.
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