Developmental Strategies in Diagnosing Obstructive Sleep Apnea
V. Santhiya1, T. Ravichandran2, G. Yamuna3, K. Harini4
1V. Santhiya*, Student of Annamalai University, Chidambaram.
2Mr. T. Ravichandran, Associate Professor, Department, Electronics and Communication Engineering, Annamalai University. Chidambaram.
3Dr. G. Yamuna, Professor and Head, Department, Electronics and Communication Engineering, Annamalai University. Chidambaram. K. Harini, Student of Annamalai University, Chidambaram.
Manuscript received on January 26, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 2868-2873 | Volume-9 Issue-3, February 2020. | Retrieval Number: B3175129219/2020©BEIESP | DOI: 10.35940/ijeat.B3175.029320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: A key mission in medical science is diagnosing a disease due to its criticality and accuracy in examining whether a patient is suffering from particular disease or not. Then, the most appropriate side of treatment can be decided. Obstructive Sleep Apnea (OSA) syndrome is the most widespread sleep disorder characterized by chronic episodes of reduction in the airflow or stoppage in airflow during sleep, being caused by blockage of upper airway. The intention of this review is to analyze already existing algorithms for detecting apnea all the way through usage of different sensors that have not been implemented on hardware. This study offers an exhaustive literature research value from 2003 to 2019 and setting a roadmap for bio-engineers and medical doctors thereby reducing research period and improving medical service efficiency concerning obstructive sleep apnea diagnosis.
Keywords: Active interactive new Neighbor rate, Energy, Fuzzy logic, Mobile Ad hoc Networks, Path Stability.