Use of an ANN to Value MTF and Melatonin Effect on ADHD Affected Children
Metadatos
Mostrar el registro completo del ítemEditorial
IEEE
Materia
Artificial neural networks Melatonin ADHD Children--Sleep
Fecha
2019-08-26Referencia bibliográfica
Muñoz, A., Palomo, E. J., & Jerez-Calero, A. (2019). Use of an ANN to Value MTF and Melatonin Effect on ADHD Affected Children. IEEE Access, 7, 127254-127264.
Resumen
Sleep disorders is one of the most frequent child medical consultation, indeed the rate of
children that suffer it in a transitory way is considerably high. Among the most common sleep disorders is
named ''children behavioral insomnia'', many different drugs has been used as treatment with poor results
with relevant secondary effects. We focus on children with ADHD that present sleep disorders among
most frequent comorbidities. The most relevant contribution of this work is the use of an artficial neural
network (ANN) for unsupervised learning called the Growing Neural Forest (GNF), which is a variation of
the Growing Neural Gas (GNG) model where a set of trees is learnt instead of a general graph so that input
data can be better represented, to study actigraphic data to evaluate the use of MTF and melatonin in a group
of children with sleep disorders. Thus, the GNF model is trained with actigraphic data from children ADHD
affected as input data. The GNG and SOM (Self-Organizing Map) models are also trained with these data
for comparative purposes. Experimental results demonstrate that sleep was not affected by administrating
drugs (MFT and melatonin).