Use of an ANN to Value MTF and Melatonin Effect on ADHD Affected Children

Abstract

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).