Luigi Pugliese, Michele Guagnano, Sara Groppo, Massimo Violante, Riccardo Groppo
{"title":"Rule-based Sleep-Apnea detection algorithm","authors":"Luigi Pugliese, Michele Guagnano, Sara Groppo, Massimo Violante, Riccardo Groppo","doi":"10.1109/IWASI58316.2023.10164530","DOIUrl":null,"url":null,"abstract":"Obstructive Sleep Apnea Syndrome (OSAS) is a common sleep disorder characterized by repeated episodes of breathing cessation during sleep. It affects the quality of life and can lead to severe health complications. Continuous monitoring of Heart Rate Variability (HRV) and oxygen saturation (SpO2) can provide valuable insights into the presence and severity of sleep apnea. The algorithm herein proposed aims to identify the presence of OSAS and then to highly accurately differentiate (Severe, Moderate or Low) its severity level. The algorithm was evaluated on an online dataset; at the end of the algorithm assessment, a correlation coefficient of 98.65% was reached.","PeriodicalId":261827,"journal":{"name":"2023 9th International Workshop on Advances in Sensors and Interfaces (IWASI)","volume":"708 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 9th International Workshop on Advances in Sensors and Interfaces (IWASI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IWASI58316.2023.10164530","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Obstructive Sleep Apnea Syndrome (OSAS) is a common sleep disorder characterized by repeated episodes of breathing cessation during sleep. It affects the quality of life and can lead to severe health complications. Continuous monitoring of Heart Rate Variability (HRV) and oxygen saturation (SpO2) can provide valuable insights into the presence and severity of sleep apnea. The algorithm herein proposed aims to identify the presence of OSAS and then to highly accurately differentiate (Severe, Moderate or Low) its severity level. The algorithm was evaluated on an online dataset; at the end of the algorithm assessment, a correlation coefficient of 98.65% was reached.