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%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hossain, J., Shapiro, C.: The prevalence, cost implications, and management of sleep disorders: an overview. Sleep Breath. 6(2), 85–102 (2002). doi:10.1007/s11325-002-0085-1
Flores, A., Flores, J., Deshpande, H., Picazo, J., Xie, X., Franken, P., Heller, H., Grahn, D., O’Hara, B.: Pattern recognition of sleep in rodents using piezoelectric signals generated by gross body movements. IEEE Trans. Biomed. Eng. 54(2), 225–233 (2007)
Xie, B., Minn, H.: Real-time sleep apnea detection by classifier combination. IEEE Trans. Inf Technol. Biomed. 16(3), 469–477 (2012)
de Mesquita, J.A., de Melo, P.L.: Respiratory monitoring system based on the nasal pressure technique for the analysis of sleep breathing disorders: reduction of static and dynamic errors, and comparisons with thermistors and pneumotachographs. Rev. Sci. Instrum. 75(3), 760–767 (2004). doi:10.1063/1.1646734
Hurwitz, J., Kaufman, M., Bowles, A.: Cognitive Computing and Big Data Analytics, 1st edn. Wiley Publishing, New York (2015)
Welton, N.J., Ades, A.E.: Estimation of Markov chain transition probabilities and rates from fully and partially observed data: uncertainty propagation, evidence synthesis, and model calibration. Med. Decis. Making 25(6), 633–645 (2005). doi:10.1177/0272989X05282637. pMID: 16282214
Coronato, A., Paragliola, G.: An approach for the evaluation of sleeping behaviors disorders in patients with cognitive diseases: a case study (2016)
Gabriel, T.R., Berthold, M.R.: Influence of fuzzy norms, other heuristics on “mixed fuzzy rule formation”. Int. J. Approximate Reasoning 35(2), 195–202 (2004). http://www.sciencedirect.com/science/article/pii/S0888613X03001427
Riedmiller, M., Braun, H.: A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: IEEE International Conference on Neural Networks, vol. 1, pp. 586–591 (1993)
Acknowledgments
The authors of this paper would like to thanks Giuseppe Trerotola and Raffaele Mattiello for the technical support.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Coronato, A., Paragliola, G. (2018). Towards a Cognitive System for the Identification of Sleep Disorders. In: De Pietro, G., Gallo, L., Howlett, R., Jain, L. (eds) Intelligent Interactive Multimedia Systems and Services 2017. KES-IIMSS-18 2018. Smart Innovation, Systems and Technologies, vol 76. Springer, Cham. https://doi.org/10.1007/978-3-319-59480-4_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-59480-4_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59479-8
Online ISBN: 978-3-319-59480-4
eBook Packages: EngineeringEngineering (R0)