Apnea Recognition with Wavelet Neural Networks
DOI:
https://doi.org/10.5540/tema.2018.019.02.277Palavras-chave:
Neural Network, Sleep Disorder Syndrome, ApneaResumo
Apnea is a Sleep Disorder Syndrome characterized by an interruption or reduction of air flow for at least 10 seconds. Polysomnography is a test used to apnea diagnosis. Several signals, including Electrocardiogram (ECG), Electroencephalogram (EEG) and Oxygen Saturation (SpO_2) are obtained in this diagnostic test. Since most tests for apnea are uncomfortable, there is an increase search for alternative methods to reduce cost and improve patient well-being.In this work, we use only SpO_2 data from 25 patients of the St Vincent's University Hospital, Dublin, to extract parameters connected to a Neural Network attempting to classify patients with apnea or non-apnea. Results confirm that our alternative method can be used as an auxiliary tool for diagnosis by using exclusively SpO_2 signal.
Referências
M. M. Baig, H. Gholamhosseini. Smart health monitoring systems: an overview of design and modeling. Journal of medical systems, n. 37, vol. 2, p. 1-14, 2013.
P. de Chazal, N. Sadr, N., M. Jayawardhana. An ECG oximetry system for identifying obstructive and central apnoea events. Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE, p. 7671-7674. IEEE, 2015.
C. Chui, Q. Jiang. Applied Mathematics: Data Compression, Spectral Fourier Analysis, Wavelets, and Applications. Mathematics Textbooks fo and Engineering. Atlantis Press, 2013.
Daubechies, I. et al. Ten lectures on wavelets, volume 61. SIAM, 1992.
J. Dheeba, N. A. Singh, S. T. Selvi. Computer-aided detection of breast cancer on mammograms: A swarm intelligence optimized wavelet neural network approach. Journal of biomedical informatics, n. 49, p. 45-52, 2015.
I. Dimitrovski, D. Kocev, I. Kitanovski, S. Loskovska, S. Deroski. Improved medical image modality classification using a combination of visual and textual features. Computerized Medical Imaging and Graphics, v. 39, p. 14-26, 2015.
L. Erazo, S. A. Riós, A benchmark on automatic obstructive sleep apnea screening algorithms in children. Procedia Computer Science, v. 35, p.739-746, 2014.
H. Espiritu, V. Metsis, Automated detection of sleep disorder-related events from polysomnographic data. Healthcare Informatics (ICHI), 2015 International Conference on, p. 562-569. IEEE, 2015.
O. Faust, U. R. Acharya, H. Adeli, A. Adeli. Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis. Seizure, n. 26, p. 56-64, 2015.
W. W. Flemons, W. T. McNicholas, Clinical prediction of the sleep apnea syndrome. Sleep medicine reviews, v. 1, n. 1, p. 19-32, 1997.
A. L. Goldberger, L. A. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C. K. Peng, H. E. Stanley. Physiobank, physiotoolkit, and physionet components of a new research resource for complex physiologic signals. Circulation, v. 101, n. 23, e215-e220, 2000.
S. Haykin. Neural Networks and Learning Machines, vol.10, Prentice Hall, 2009.
G. B. Huang, L. Chen, C. K. Siew. Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Transactions on Neural Networks v. 17, n.4, p. 879-892, 2006.
Kaiser, G. A friendly guide to wavelets. Springer Science and Business Media, 2010.
L. C. Lemos, E. C. Marqueze, F. Sachi, G. Lorenzi-Filho, C. R. de C. Moreno. Síndrome da apnéia obstrutiva do sono em motoristas de caminhão. J Bras Pneumol, v. 35, n. 6, p. 500-506, 2009.
J. S. Lim. Two-dimensional signal and image processing. Englewood Cliffs, NJ,Prentice Hall, p. 469-476, v. 1, 1990.
Mallat, S. A Wavelet Tour of Signal Processing, Third Edition: The Spa Academic Press, 3rd edition, 2008.
M. F. Møller. A scaled conjugate gradient algorithm for fast supervised learning. Neural networks, v. 6, n. 4, p. 525-533, 1993.
Y. Nievergelt. Wavelets Made Easy. Birkhauser, 1999.
V. P. Nigam, D. Graupe. A neural-network-based detection of epilepsy. Neurological Research, 2013.
T. Penzel, J. McNames, P. De Chazal, B. Raymond, A. Murray, G. Moody. Systematic comparison of different algorithms for apnoea detection based on electrocardiogram recordings. Medical and Biological Engineering and Computing, v. 40, n. 4, p. 402-407, 2002.
S. Tufik, R. Santos-Silva, J. A. Taddei, L. R. A. Bittencourt. Obstructive Sleep Apnea Syndrome in the Sao Paulo Epidemiologic Sleep Study. Sleep Medicine, v. 11, n. 5, p. 1389-9457, 2010.
UCDDB. St. Vincent’s University Hospital/University College Dublin Sleep Apnea Database, 2008. Url: http://physionet.org/physiobank/ database/ucddb/.
Downloads
Publicado
Como Citar
Edição
Seção
Licença
Direitos Autorais
Autores de artigos publicados no periódico Trends in Computational and Applied Mathematics mantêm os direitos autorais de seus trabalhos. O periódico utiliza a Atribuição Creative Commons (CC-BY) nos artigos publicados. Os autores concedem ao periódico o direito de primeira publicação.
Propriedade Intelectual e Termos de uso
O conteúdo dos artigos é de responsabilidade exclusiva dos autores. O periódico utiliza a Atribuição Creative Commons (CC-BY) nos artigos publicados. Esta licença permite que os artigos publicados sejam reutilizados sem permissão para qualquer finalidade, desde que o trabalho original seja corretamente citado.
O periódico encoraja os Autores a autoarquivar seus manuscritos aceitos, publicando-os em blogs pessoais, repositórios institucionais e mídias sociais acadêmicas, bem como postando-os em suas mídias sociais pessoais, desde que seja incluída a citação completa à versão do website da revista.
