基于超宽带人工智能床垫的新型睡眠呼吸暂停检测

Chiapin Wang, Jen-Hau Chan, Shih-Hau Fang, Ho-Ti Cheng, Yeh-Liang Hsu
{"title":"基于超宽带人工智能床垫的新型睡眠呼吸暂停检测","authors":"Chiapin Wang, Jen-Hau Chan, Shih-Hau Fang, Ho-Ti Cheng, Yeh-Liang Hsu","doi":"10.1109/AICAS.2019.8771598","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a novel sleep apnea identification system by adopting a sleep breathing monitoring mattress which utilizes the ultra-wideband (UWB) physiological sensing technique. Unlike traditional methods which need wearable devices and electrical equipment connected to patients, the proposed system detects apnea in a non-conscious and non-contact way by using UWB sensors. The proposed system is built by a machine learning technique in the offline stage, and detects apnea in the online stage by using our designed apnea detection algorithm. The experimental results illustrate that the proposed apnea identification system efficiently detects sleep apnea without diagnoses undertaken at hospitals.","PeriodicalId":273095,"journal":{"name":"2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Novel Sleep Apnea Detection Based on UWB Artificial Intelligence Mattress\",\"authors\":\"Chiapin Wang, Jen-Hau Chan, Shih-Hau Fang, Ho-Ti Cheng, Yeh-Liang Hsu\",\"doi\":\"10.1109/AICAS.2019.8771598\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose a novel sleep apnea identification system by adopting a sleep breathing monitoring mattress which utilizes the ultra-wideband (UWB) physiological sensing technique. Unlike traditional methods which need wearable devices and electrical equipment connected to patients, the proposed system detects apnea in a non-conscious and non-contact way by using UWB sensors. The proposed system is built by a machine learning technique in the offline stage, and detects apnea in the online stage by using our designed apnea detection algorithm. The experimental results illustrate that the proposed apnea identification system efficiently detects sleep apnea without diagnoses undertaken at hospitals.\",\"PeriodicalId\":273095,\"journal\":{\"name\":\"2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)\",\"volume\":\"49 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-03-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AICAS.2019.8771598\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AICAS.2019.8771598","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

摘要

本文提出了一种基于超宽带生理传感技术的睡眠呼吸监测床垫的睡眠呼吸暂停识别系统。传统方法需要可穿戴设备和电气设备连接到患者身上,与此不同,该系统通过使用超宽带传感器以无意识和非接触的方式检测呼吸暂停。该系统在离线阶段采用机器学习技术,在在线阶段采用我们设计的呼吸暂停检测算法进行呼吸暂停检测。实验结果表明,所提出的呼吸暂停识别系统可以有效地检测睡眠呼吸暂停,而无需在医院进行诊断。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Novel Sleep Apnea Detection Based on UWB Artificial Intelligence Mattress
In this paper, we propose a novel sleep apnea identification system by adopting a sleep breathing monitoring mattress which utilizes the ultra-wideband (UWB) physiological sensing technique. Unlike traditional methods which need wearable devices and electrical equipment connected to patients, the proposed system detects apnea in a non-conscious and non-contact way by using UWB sensors. The proposed system is built by a machine learning technique in the offline stage, and detects apnea in the online stage by using our designed apnea detection algorithm. The experimental results illustrate that the proposed apnea identification system efficiently detects sleep apnea without diagnoses undertaken at hospitals.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Artificial Intelligence of Things Wearable System for Cardiac Disease Detection Fast event-driven incremental learning of hand symbols Accelerating CNN-RNN Based Machine Health Monitoring on FPGA Neuromorphic networks on the SpiNNaker platform Complexity Reduction on HEVC Intra Mode Decision with modified LeNet-5
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1