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Reseach Article

A Fuzzy Inference Approach for the Diagnosis of Sleep Disorders

by Vijay Kumar Garg, R.k. Bansal
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 110 - Number 2
Year of Publication: 2015
Authors: Vijay Kumar Garg, R.k. Bansal
10.5120/19285-0703

Vijay Kumar Garg, R.k. Bansal . A Fuzzy Inference Approach for the Diagnosis of Sleep Disorders. International Journal of Computer Applications. 110, 2 ( January 2015), 1-4. DOI=10.5120/19285-0703

@article{ 10.5120/19285-0703,
author = { Vijay Kumar Garg, R.k. Bansal },
title = { A Fuzzy Inference Approach for the Diagnosis of Sleep Disorders },
journal = { International Journal of Computer Applications },
issue_date = { January 2015 },
volume = { 110 },
number = { 2 },
month = { January },
year = { 2015 },
issn = { 0975-8887 },
pages = { 1-4 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume110/number2/19285-0703/ },
doi = { 10.5120/19285-0703 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:45:17.421712+05:30
%A Vijay Kumar Garg
%A R.k. Bansal
%T A Fuzzy Inference Approach for the Diagnosis of Sleep Disorders
%J International Journal of Computer Applications
%@ 0975-8887
%V 110
%N 2
%P 1-4
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Sleep plays a vital role in the life of human being and in convention of neuro-science. But some time, sleep is disrupted along with unusual behaviors associated with it. A numerous techniques and methods are adopted by many researchers for the diagnosis of disruptions to sleep along with the other sleep disorders and also for the diagnosis of unusual behavior linked with sleep that can also increase the sleep disruptions. In this paper, a fuzzy inference system (FIS) is developed for the diagnosis of sleep disorders like Sleep Apnea, Insomina, Parasomnia and Snoring. The dataset considered in this study is collected from various physicians. To construct the fuzzy inference system, three membership functions are used like low, medium and high. The range for all these membership functions is set according to their importance in the respective disease. A record of 140 patients is considered in this work. The accuracy achieved from the proposed system is 89. 2%.

References
  1. G. Guimaraes, J. -H. peter, T. Penzel, A. Ultsch, A method for automated temporal knowledge acquisition applied to sleep-relaed breathing disorders, artificial Intelligence in Medicine, vol. 23, 2001, pp. 211-237.
  2. Yashar Maali, A novel partially connected cooperative parallel PSO-SVM algorithm: Study based on sleep apnea detection, in: Proceedings of IEEE Congress on Evolutionary Computation (CEC), 10-15, June 2012, pp. 1-8.
  3. D Liu, Z Pang, SR Lloyd, A Neural Network Method for Detection of Obstructive Sleep Apnea and Narcolepsy Based on Pupil Size and EEG, IEEE Transactions on Neural Networks, vol. 19, Issue 2, 2008, pp. 308-318.
  4. Causa L. , Held, C. M. , Causa, J. , Estevez, P. A. , Perez, C. A. , Chamorro, R. , Garrido, M. , Algarin, C. , Peirano, P. , Automated Sleep-Spindle Detection in Healthy Children Polysomnograms, IEEE Transactions on Biomedical Engineering, vol. 57, Issue 9, Sept. 2010, pp. 2135-2146.
  5. Diego Alvarez-Estevez, Jose M. Fernández-Pastoriza , Elena Hernández-Pereira , Vicente Moret-Bonill, A method for the automatic analysis of the sleep macrostructure in continuum, Elsevier: Expert Systems with Applications, vol. 40, Issue 5, April 2012, pp. 1796–1803.
  6. Alvarez-Estevez D. , Fernandez-Pastoriza, J. M. , Moret-Bonillo, A continuous evaluation of the awake sleep state using fuzzy reasoning, in: Proceedings of Annual International Conference of IEEE on Engineering in Medicine and Biology Society, EMBC, 3-6 Sept. 2009, pp. 5539-5542.
  7. Diego Alvarez-Estevez, Vicente Moret-Bonillo, Fuzzy reasoning used to detect apneic events in the sleep apnea-hypopnea syndrome, Elsevier: Expert Systems with Applications, vol. 36, Issue 4, May 2009, pp. 7778–7785.
  8. Pedro Pinero, Pavel Garciaa, Leticia Arco, Alfredo Álvarezc, M. Matilde Garc?iab, Rolando Bona, Sleep stage classification using fuzzy sets and machine learning techniques, Elsevier: Neurocomputing, vol. 58–60, June 2004, pp. 1137-1143.
  9. Azadeh Yadollahi, Zahra Moussavi, Acoustic Obstructive sleep apnea detection, in: Proceedings of 31st Annual International Conference of the IEEE EMBS Minneapolis, Minnesota, USA, 2009, September 2-6.
  10. Sleep Disorder Overview. www. neurologychannel. com
  11. Sheng-Fu Liang, Ying-Huang Chen, Chih-En Kuo, Jyun-Yu Chen, Sheng-Che Hsu, A fuzzy inference system for sleep staging, in: Proceedings of IEEE International Conference on Fuzzy Systems (FUZZ), 27-30 June 2011, pp. 2104-2107.
Index Terms

Computer Science
Information Sciences

Keywords

Fuzzy Logic Membership Functions Sleep Disorders