Yingjian Song, Bingnan Li, Dan Luo, Glenna S Brewster Glasgow, Bradley G Phillips, Yuan Ke, Wenzhan Song
{"title":"利用非接触式床震图进行实时连续血压估算","authors":"Yingjian Song, Bingnan Li, Dan Luo, Glenna S Brewster Glasgow, Bradley G Phillips, Yuan Ke, Wenzhan Song","doi":"10.1109/icc51166.2024.10622995","DOIUrl":null,"url":null,"abstract":"<p><p>In this study, we introduce BedDot, the first contact-free and bed-mounted continuous blood pressure monitoring sensor. Equipped with a seismic sensor, BedDot eliminates the need for external wearable devices and physical contact, while avoiding privacy or radiation concerns associated with other technologies such as cameras or radars. Using advanced preprocessing techniques and innovative AI algorithms, we extract time-series features from the collected bedseismogram signals and accurately estimate blood pressure with remarkable stability and robustness. Our user-friendly prototype has been tested with over 75 participants, demonstrating exceptional performance that meets all three major industry standards, which are Association for the Advancement of Medical Instrumentation (AAMI), Food and Drug Administration (FDA) and the British and Irish Hypertension Society (BHS), and outperforms current state-of-the-art deep learning models for time series analysis. As a non-invasive solution for monitoring blood pressure during sleep and assessing cardiovascular health, BedDot holds immense potential for revolutionizing the field.</p>","PeriodicalId":93294,"journal":{"name":"IEEE International Conference on Communications : [proceedings]. IEEE International Conference on Communications","volume":"2024 ","pages":"214-219"},"PeriodicalIF":0.0000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11583795/pdf/","citationCount":"0","resultStr":"{\"title\":\"Real-time Continuous Blood Pressure Estimation with Contact-free Bedseismogram.\",\"authors\":\"Yingjian Song, Bingnan Li, Dan Luo, Glenna S Brewster Glasgow, Bradley G Phillips, Yuan Ke, Wenzhan Song\",\"doi\":\"10.1109/icc51166.2024.10622995\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>In this study, we introduce BedDot, the first contact-free and bed-mounted continuous blood pressure monitoring sensor. Equipped with a seismic sensor, BedDot eliminates the need for external wearable devices and physical contact, while avoiding privacy or radiation concerns associated with other technologies such as cameras or radars. Using advanced preprocessing techniques and innovative AI algorithms, we extract time-series features from the collected bedseismogram signals and accurately estimate blood pressure with remarkable stability and robustness. Our user-friendly prototype has been tested with over 75 participants, demonstrating exceptional performance that meets all three major industry standards, which are Association for the Advancement of Medical Instrumentation (AAMI), Food and Drug Administration (FDA) and the British and Irish Hypertension Society (BHS), and outperforms current state-of-the-art deep learning models for time series analysis. As a non-invasive solution for monitoring blood pressure during sleep and assessing cardiovascular health, BedDot holds immense potential for revolutionizing the field.</p>\",\"PeriodicalId\":93294,\"journal\":{\"name\":\"IEEE International Conference on Communications : [proceedings]. IEEE International Conference on Communications\",\"volume\":\"2024 \",\"pages\":\"214-219\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11583795/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE International Conference on Communications : [proceedings]. IEEE International Conference on Communications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/icc51166.2024.10622995\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/8/20 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE International Conference on Communications : [proceedings]. IEEE International Conference on Communications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/icc51166.2024.10622995","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/8/20 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
Real-time Continuous Blood Pressure Estimation with Contact-free Bedseismogram.
In this study, we introduce BedDot, the first contact-free and bed-mounted continuous blood pressure monitoring sensor. Equipped with a seismic sensor, BedDot eliminates the need for external wearable devices and physical contact, while avoiding privacy or radiation concerns associated with other technologies such as cameras or radars. Using advanced preprocessing techniques and innovative AI algorithms, we extract time-series features from the collected bedseismogram signals and accurately estimate blood pressure with remarkable stability and robustness. Our user-friendly prototype has been tested with over 75 participants, demonstrating exceptional performance that meets all three major industry standards, which are Association for the Advancement of Medical Instrumentation (AAMI), Food and Drug Administration (FDA) and the British and Irish Hypertension Society (BHS), and outperforms current state-of-the-art deep learning models for time series analysis. As a non-invasive solution for monitoring blood pressure during sleep and assessing cardiovascular health, BedDot holds immense potential for revolutionizing the field.