Citation: Position Papers of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 12, pages 43–48 (2017)
Abstract. Alzheimer's disease is the most common type of dementia. Patients suffer from of this kind of disease could show symptoms such as sleep disturbances, muscle rigidity or other typical Alzheimer's movement irregularities. In our work, we have focused on those types of disturbances related to sleep disorders. Due to their not well-known nature, it is difficult to develop software able to identify sleep disorders. In this work, we have addressed the problem of the automatic recognition of sleep disorders in patients with Alzheimer's disease by using deep learning algorithms.
- E. A. Hahn, H.-X. Wang, R. Andel, and L. Fratiglioni, “A change in sleep pattern may predict alzheimer disease,” The American Journal of Geriatric Psychiatry, vol. 22, no. 11, pp. 1262 – 1271, 2014, physical Comorbidity. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1064748113002339
- J. Cedernaes, R. S. Osorio, A. W. Varga, K. Kam, H. B. Schith, and C. Benedict, “Candidate mechanisms underlying the association between sleep-wake disruptions and alzheimer’s disease,” Sleep Medicine Reviews, vol. 31, pp. 102 – 111, 2017. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1087079216000186
- J. E. Smith and J. M. Tolson, “Recognition, diagnosis, and treatment of restless legs syndrome,” Journal of the American Academy of Nurse Practitioners, vol. 20, no. 8, pp. 396–401, 2008. [Online]. Available: http://dx.doi.org/10.1111/j.1745-7599.2008.00337.x
- A. Coronato and G. Paragliola, “A structured approach for the designing of safe aal applications,” Expert Systems with Applications, vol. 85, pp. 1–13, 2017.
- A. Coronato and G. De Pietro, “Situation awareness in applications of ambient assisted living for cognitive impaired people,” Mobile Networks and Applications, pp. 1–10, 2013.
- A. Coronato and G. Paragliola, “An approach for the evaluation of sleeping behaviors disorders in patients with cognitive diseases: A case study,” in 2016 12th International Conference on Signal-Image Technology Internet-Based Systems (SITIS), Nov 2016, pp. 545–550.
- “Empatica.” [Online]. Available: https://www.empatica.com/get-started-e4
- D. Rav, C. Wong, F. Deligianni, M. Berthelot, J. Andreu-Perez, B. Lo, and G. Z. Yang, “Deep learning for health informatics,” IEEE Journal of Biomedical and Health Informatics, vol. 21, no. 1, pp. 4–21, Jan 2017.
- F. Poree, A. Kachenoura, H. Gauvrit, C. Morvan, G. Carrault, and L. Senhadji, “Blind source separation for ambulatory sleep recording,” IEEE Transactions on Information Technology in Biomedicine, vol. 10, no. 2, pp. 293–301, April 2006.
- M. Z. Islam, K. M. T. Nahiyan, and M. A. Kiber, “A motion detection algorithm for video-polysomnography to diagnose sleep disorder,” in 2015 18th International Conference on Computer and Information Technology (ICCIT), Dec 2015, pp. 272–275.
- A. E. Flores, J. E. Flores, H. Deshpande, J. A. Picazo, X. Xie, P. Franken, H. C. Heller, D. A. Grahn, and B. F. O’Hara, “Pattern recognition of sleep in rodents using piezoelectric signals generated by gross body movements,” IEEE Transactions on Biomedical Engineering, vol. 54, no. 2, pp. 225–233, Feb 2007.
- R. Prashanth, S. D. Roy, P. K. Mandal, and S. Ghosh, “Parkinson’s disease detection using olfactory loss and rem sleep disorder features,” in 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Aug 2014, pp. 5764–5767.
- J. Alves de Mesquita and P. Lopes de Melo, “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,” Review of Scientific Instruments, vol. 75, no. 3, pp. 760–767, 2004. [Online]. Available: http://scitation.aip.org/content/aip/journal/rsi/75/3/10.1063/1.1646734
- C. Occhiuzzi and G. Marrocco, “The rfid technology for neurosciences: Feasibility of limbs’ monitoring in sleep diseases,” IEEE Transactions on Information Technology in Biomedicine, vol. 14, no. 1, pp. 37–43, Jan 2010.
- J. Park, D. Kim, C. Yang, and H. Ko, “Svm based dynamic classifier for sleep disorder monitoring wearable device,” in 2016 IEEE International Conference on Consumer Electronics (ICCE), Jan 2016, pp. 309–310.
- Y. Bengio, “Practical recommendations for gradient-based training of deep architectures,” CoRR, vol. abs/1206.5533, 2012. [Online]. Available: http://arxiv.org/abs/1206.5533
- G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Improving neural networks by preventing co-adaptation of feature detectors,” CoRR, vol. Abs/1207.0580, 2012. [Online]. Available: http://arxiv.org/abs/1207.0580
- Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.
- A. Testa and A. Coronato, “A review of the methods for the dependability assessment of wsns: Towards a new approach.” Adhoc & Sensor Wireless Networks, vol. 33, 2016.
- A. Testa, M. Cinque, A. Coronato, G. De Pietro, and J. C. Augusto, “Heuristic strategies for assessing wireless sensor network resiliency: an event-based formal approach,” Journal of Heuristics, vol. 21, no. 2, p. 145, 2015.