A. Tataraidze, L. Anishchenko, L. Korostovtseva, M. Bochkarev, Y. Sviryaev
{"title":"Non-contact Respiratory Monitoring of Subjects with Sleep-Disordered Breathing","authors":"A. Tataraidze, L. Anishchenko, L. Korostovtseva, M. Bochkarev, Y. Sviryaev","doi":"10.1109/ITMQIS.2018.8525001","DOIUrl":null,"url":null,"abstract":"Long-term sleep monitoring might be helpful for biological, somnological and pharmaceutical studies. One of the most important task in this field is automated sleep structure estimation. As was shown in recent studies, it is possible to detect sleep stages of healthy subjects based on the analysis of respiratory patterns. However, it is still a question whether it is achievable for other cohorts. This paper presents an algorithm for breathing cycle detection on non-contact bioradiolocation signals for subjects with sleep-disordered breathing (SDB), which is the first step in the development of a technology for continuous sleep monitoring for the SDB population. The quality of the proposed algorithm was estimated based on comparison with a standard contact respiratory sensor. Our results contribute to extension of non-contact sleep monitoring technology.","PeriodicalId":133622,"journal":{"name":"2018 IEEE International Conference \"Quality Management, Transport and Information Security, Information Technologies\" (IT&QM&IS)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE International Conference \"Quality Management, Transport and Information Security, Information Technologies\" (IT&QM&IS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITMQIS.2018.8525001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11
Abstract
Long-term sleep monitoring might be helpful for biological, somnological and pharmaceutical studies. One of the most important task in this field is automated sleep structure estimation. As was shown in recent studies, it is possible to detect sleep stages of healthy subjects based on the analysis of respiratory patterns. However, it is still a question whether it is achievable for other cohorts. This paper presents an algorithm for breathing cycle detection on non-contact bioradiolocation signals for subjects with sleep-disordered breathing (SDB), which is the first step in the development of a technology for continuous sleep monitoring for the SDB population. The quality of the proposed algorithm was estimated based on comparison with a standard contact respiratory sensor. Our results contribute to extension of non-contact sleep monitoring technology.