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Towards a Cognitive System for the Identification of Sleep Disorders

  • Antonio Coronato
  • Giovanni ParagliolaEmail author
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 76)

Abstract

Alzheimer’s disease (AD) is the most common type of dementia. Patients with AD may show anomalous behaviors such as sleeping disorders. Due to the increasing attention focused on these kinds of behaviors, activities like monitoring and identification are becoming critical. In order to meet these requirements, we propose a cognitive approach based on a combination of machine learning algorithms and a prior knowledge base for the identification of anomalous behaviors during sleep. The results show an improvement in the identification of sleeping disorders of more than 10%.

Keywords

Sleep Disorder Classification Process Anomalous Behavior Machine Learning Algorithm Sleep Disorder 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The authors of this paper would like to thanks Giuseppe Trerotola and Raffaele Mattiello for the technical support.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.National Research Council (CNR) Institute for High-Performance Computing and Networking (ICAR)NapoliItaly

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