PATTERN IDENTIFICATION IN SLEEP APNEA
Main Article Content
Keywords
Abstract
Sleep apnea is a serious sleep disorder that is characterized by repeated interruptions in breathing during sleep, which can result in a variety of negative health consequences. It is defined by abnormal breathing pauses or restrictions during sleep, and is estimated to affect 2% of middle-aged women and 4% of middle-aged men. Although sleep apnea is treatable, approximately 90% of sufferers go undiagnosed and therefore untreated. This can result in daytime sleepiness and fatigue, which can lead to traffic accidents, depression, and memory loss. De- spite advances in diagnosis and treatment, much remains to be understood about the causes and most effective management of this disorder. This research paper aims to provide an overview of sleep apnea research, covering its causes, diagnosis, and treatment. The standard diagnostic method for sleep apnea and hypopnea syndrome (SAHS) is polysomnography. A common diagnostic method for sleep disorders involves patients spending a night or two in a sleep laboratory with sensors and wires attached to their bodies. The signals recorded during the test are then analyzed by a sleep specialist in order to make an accurate diagnosis. How- ever, this method can be inconvenient, uncomfortable, and expensive, which can discourage people from seeking a diagnosis. To overcome these challenges, researchers have ex- plored alternative diagnostic methods that are readily available, affordable, and reliable. These methods include portable monitoring devices, questionnaires, and home sleep testing. For instance, the PhysioNet/Cardiology Computational Challenge 2018 utilized various physiological signals, such as EEG, EOG, EMG, ECG, and SaO2, to study sleep apnea. The American Academy of Sleep Medicine (AASM) rec- ommends that stimulation is recorded within a 30-second window to allow for a broad baseline that can account for EEG frequency shifts. However, the strength and duration of this fundamental sequence are not precisely defined. One study used polysomnography to identify the cause of wakefulness during sleep without apnea. In conclusion, the diagnosis and treatment of sleep apnea remain an active area of research, with ongoing efforts to improve diagnostic accuracy and develop more effective treatment options. Although polysomnography is still the most reliable method of diagnosis, there is ongoing research to find alternative diagnostic techniques that are more accessible and less burdensome for patients. The paper will also discuss the latest findings and future research directions in this area. The goal is to provide a comprehensive understanding of the current state of sleep apnea research and highlight areas where further research is needed to improve the diagnosis and treatment of this debilitating disease.
Downloads
References
[1] E. Smily JeyaJothi, J. Anitha, Shalli Rani, Basant Tiwari, "A Com- prehensive Review: Computational Models for Obstructive Sleep Apnea Detection in Biomedical Applications", BioMed Research International 2022. [2] Liu, Y., Liu, H., & Yang, B. (2020). Automatic Sleep Arousals Detection From Polysomnography Using Multi-Convolution Neural Network and Random Forest. IEEE Access,2020 [3] Plesinger, F., Nejedly, P., Viscor, I., Andrla, P., Halamek, J., & Jurak, P. (2018). Automated Sleep Arousal Detection Based on EEG Envel- ograms. 2018 Computing in Cardiology Conference (CinC).2018 [4] Ghassemi, M., Moody, B., Lehman, L., Song, C., Li, Q., Sun, H., . . . Clifford, G. (2018). You Snooze, You Win: The PhysioNet/Computing in Cardiology Challenge 2018. 2018 Computing in Cardiology Con- ference (CinC).2018.049 [5] Ramachandran, A., & Karuppiah, A. A Survey on Recent Advances in Machine Learning Based Sleep Apnea Detection Systems. Health- care, 9(7), 914.(2021) [6] Xie, B., Hlaing Minn. Real-Time Sleep Apnea Detection by Classifier Combination. IEEE Transactions on Information Technology in Biomedicine, 16(3), 469–477.(2012). [7] Chambon, S., Galtier, M. N., Arnal, P. J., Wainrib, G., & Gram- fort, A. . A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26(4), 758–769.(2018) [8] Kemp, B., Zwinderman, A. H., Tuk, B., Kamphuisen, H. A. C., & Oberye, J. J. L. Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG. IEEE Transactions on Biomedical Engineering, 47(9), 1185–1194.(2000). [9] Li, Hongyang and Guan, Yuanfang, Deepsleep: Fast and Accurate Delineation of Sleep Arousals at Millisecond Resolution by Deep Learning (08/29/2019) [10] Jehan Pratama Herdaning1, Sugondo Hadiyoso2 and Inung Wi- jayanto1,"Analysis Of Alpha And Beta EEG Signal Pattern In Try- pophobia Condition With Wavelet Method",Jehan Pratama Herdan- ing et al (2019) [11] A. L. Goldberger, L. A. N. Amaral, L. Glass, and J. M. Hausdorff, “PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals,” Circulation, vol. 101, no. 23, pp. e215–e220, (2003). [12] G. Parati, C. Lombardi, and K. Narkiewicz, “Sleep apnea: Epidemi- ology, pathophysiology, and relation to cardiovascular risk,” Amer. J. Physiol.-Regulatory, Integrative Comparative Physiol., vol. 293, no. 4, pp. R1671–R1683, (2007). [13] ] H. M. Práinsson, H. Ragnarsdóttir, G. F. Kristjánsson, B. Marinós- son, E. Finnsson, E. Gunnlaugsson, S. Æ. Jónsson, J. S. Ágústsson, and H. Helgadóttir, “Automatic detection of target regions of res- piratory effortrelated arousals using recurrent neural networks,” in Proc. Comput. Cardiol. Conf. (CinC), Sep. , pp. 1–4(2018). [14] S. P. Cho, J. Lee, H. D. Park, and K. J. Lee, “Detection of arousals in patients with respiratory sleep disorders using a single channel EEG,” in Proc. IEEE Eng. Med. Biol. 27th Annu. Conf., Jun., pp. 2733–2735.(2005) [15] B. Sahiner, W. Chen, and A. Pezeshk, “Comparison of two classifiers when the data sets are imbalanced: The power of the area under the precisionrecall curve as the figure of merit versus the area under the ROC curve” Proc. SPIE, vol. 10136, Mar. , Art. no. 101360G.(2017)
Article Sidebar
Article Details
How to Cite
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Transfer of Copyright and Permission to Reproduce Parts of Published Papers.
Authors retain the copyright for their published work. No formal permission will be required to reproduce parts (tables or illustrations) of published papers, provided the source is quoted appropriately and reproduction has no commercial intent. Reproductions with commercial intent will require written permission and payment of royalties.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.