Author : Vijay Kumar Garg 1
Date of Publication :20th February 2018
Abstract: This paper is based on the analysis of various soft computing techniques for the diagnosis of sleep disorders especially sleep apnea, insomnia, parasomnia and snoring on the basis of following three performance parameters: accuracy, sensitivity and specificity. Many techniques and methods are adopted by researchers to implement and diagnose these sleep disorders. The current traditional technique used to diagnose these sleep disorders is polysomnography which is costly and requires human specialists and done in unique labs. Subsequently, there is a need of a more comfortable and less expensive technique to detect such types of disorders. As of late analysts concentrated on signal processing and pattern recognition as substitute modes to reveal them. The following research is focused on the detection of sleep disorders using ECG signals by applying Tunable Q-Factor wavelet transform (TQWT) and accuracy achieved by this method is 94.2%.
Reference :
-
- [1] Stanley J. Swierzewski ' Sleep Disorders' available at http://www.healthcommunities.com/overview of-sleep
- [2] disorders.shtml (accessed on 7 March 2013).
- Klink M., Quan S. F. (Apr 1987) 'Prevalence of reported sleep disturbances in a general adult population and their relationship to obstructive airways diseases' Chest, vol. 91, pp. 540-546.
- Guimaraes G. et al. (2001) 'A method for automated temporal knowledge acquisition applied to sleep related breathing disorders' artificial Intelligence in Medicine, vol. 23, pp. 211-237.
- Carolina Varon, Alexander Caicedo, Dries Testelmans, Bertien Buyse, Sabine Van Huffel, ‘A novel algorithm for the automatic detection of sleep apnea from single-lead ECG’ DOI 10.1109/TBME.2015. 2422378, IEEE Transactions on Biomedical Engineering.
- Baile Xie, Hlaing Minn, Real-Time Sleep Apnea Detection by Classifier Combination, IEEE transactions on information technology in biomedicine, Vol. 16, NO. 3, May 2012, pp. 469-477.
- B. Tucker Woodson, Joseph K. Han, ‘Relationship of Snoring and Sleepiness as Presenting Symptoms in a Sleep Clinic Population’ Annals of Otology, Rhinology & Laryngolog, Vol. 114, Issue 10, pp. 762-767, pp. 2005
- Xuan-Lan Nguyen, Joel Chaskalovic, Dominique Rakotonanahary, Bernard Fleury, ‘Insomnia symptoms and CPAP [9] compliance in OSAS patients: A descriptive study using Data Mining methods, Sleep Medicine Vol. 11, 2010, pp. 777–784.
- Carlos H. Schenck, Jeffrey L. Boyd and Mark W. Mahowald, ‘Parasomnias: A Parasomnia Uverlap Disorder Involving Sleepwalking, Sleep Terrors, and REM Sleep Behavior Disorder in 33 Polysomnographically Confirmed Cases, Sleep, vol. 20, issue 11, pp. 972-981, 1997.
- M Cavusoglu, M Kamasak, O Erogul, T Ciloglu, Y Serinagaoglu and T Akcam, ‘An efficient method for snore/nonsnore classification of sleep sounds’ Physiological Measurement, Vol. 28, 2007, pp. 841–853.
- Changyue Song, Kaibo Liu, Xi Zhang, Lili Chen, Xiaochen Xian, ‘An Obstructive Sleep Apnea Detection Approach Using a Discriminative Hidden Markov Model from ECG Signals’ DOI 10.1109/TBME.2015.2498199, IEEE Transactions on Biomedical Engineering.