Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/31156
Título: | An artificial neuronal network approach to diagnosis of Attention Deficit Hyperactivity Disorder |
Autor(es): | Pereira, Sónia Gomes, Sabino Vicente, Henrique Ribeiro, Jorge Abelha, António Novais, Paulo Machado, José Manuel Neves, José |
Palavras-chave: | ActiGraph´s images Attention Deficit Hyperactivity Disorder Logic programming Knowledge representation and reasoning Artificial neuronal networks |
Data: | 2014 |
Editora: | IEEE |
Resumo(s): | On the one hand about 3% to 12% of school-aged children present Attention Deficit Hyperactivity Disorder (ADHD), a situation that is characterized by attention deficit, impulsiveness and restlessness, coming from a change in the neurotransmitters of the central nervous system, caused by psychological messes, environment effects or genetic characteristics. One the other hand, when one ́s aim is the prediction of ADHD in children and teenagers, we need to be able to handle incomplete or default data, like the one in ActiGraph ́s images that may exhibit potential disordered sleep patterns. Indeed, using a new approach to knowledge representation and reasoning based on Logic Programming, complemented with a computational framework based on Artificial Neural Networks, ActiGraph’s pioneering actigraphy monitoring systems may deliver, on the fly, real world information about sleep/wake behavior, circadian rhythms, daytime physical activity, and environmental light intensity for the study and clinical assessment of sleep disorders and the relationship between sleep and chronic disease. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/31156 |
ISBN: | 978-1-4799-5220-5 |
Arbitragem científica: | yes |
Acesso: | Acesso restrito UMinho |
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Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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ist2014.pdf Acesso restrito! | 350,17 kB | Adobe PDF | Ver/Abrir |