Graph-based depth video denoising and event detection for sleep monitoring

Cheng Yang, Yu Mao, Gene Cheung, V. Stanković, Kevin Chan
{"title":"Graph-based depth video denoising and event detection for sleep monitoring","authors":"Cheng Yang, Yu Mao, Gene Cheung, V. Stanković, Kevin Chan","doi":"10.1109/MMSP.2014.6958802","DOIUrl":null,"url":null,"abstract":"Quality of sleep greatly affects a person's physiological well-being. Traditional sleep monitoring systems are expensive in cost and intrusive enough that they disturb the natural sleep of clinical patients. In our previous work, we proposed a non-intrusive sleep monitoring system to first record depth video in real-time, then offline analyze recorded depth data to track a patient's chest and abdomen movements over time. Detection of abnormal breathing is then interpreted as episodes of apnoea or hypopnoea. Leveraging on recent advances in graph signal processing (GSP), in this paper we propose two new additions to further improve our sleep monitoring system. First, temporal denoising is performed using a block motion vector smoothness prior expressed in the graph-signal domain, so that unwanted temporal flickering can be removed. Second, a graph-based event classification scheme is proposed, so that detection of apnoea / hypopnoea can be performed accurately and robustly. Experimental results show first that graph-based temporal denoising scheme outperforms an implementation of temporal median filter in terms of flicker removal. Second, we show that our graph-based event classification scheme is noticeably more robust to errors in training data than two conventional implementations of support vector machine (SVM).","PeriodicalId":164858,"journal":{"name":"2014 IEEE 16th International Workshop on Multimedia Signal Processing (MMSP)","volume":"184 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE 16th International Workshop on Multimedia Signal Processing (MMSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MMSP.2014.6958802","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 18

Abstract

Quality of sleep greatly affects a person's physiological well-being. Traditional sleep monitoring systems are expensive in cost and intrusive enough that they disturb the natural sleep of clinical patients. In our previous work, we proposed a non-intrusive sleep monitoring system to first record depth video in real-time, then offline analyze recorded depth data to track a patient's chest and abdomen movements over time. Detection of abnormal breathing is then interpreted as episodes of apnoea or hypopnoea. Leveraging on recent advances in graph signal processing (GSP), in this paper we propose two new additions to further improve our sleep monitoring system. First, temporal denoising is performed using a block motion vector smoothness prior expressed in the graph-signal domain, so that unwanted temporal flickering can be removed. Second, a graph-based event classification scheme is proposed, so that detection of apnoea / hypopnoea can be performed accurately and robustly. Experimental results show first that graph-based temporal denoising scheme outperforms an implementation of temporal median filter in terms of flicker removal. Second, we show that our graph-based event classification scheme is noticeably more robust to errors in training data than two conventional implementations of support vector machine (SVM).
基于图的深度视频去噪和睡眠监测事件检测
睡眠质量极大地影响一个人的生理健康。传统的睡眠监测系统成本昂贵,干扰性强,会干扰临床患者的自然睡眠。在我们之前的工作中,我们提出了一种非侵入式睡眠监测系统,首先实时记录深度视频,然后离线分析记录的深度数据,以跟踪患者的胸部和腹部随时间的运动。检测到异常呼吸则被解释为呼吸暂停或呼吸不足发作。利用图形信号处理(GSP)的最新进展,在本文中,我们提出了两个新的补充,以进一步改进我们的睡眠监测系统。首先,使用在图信号域中表示的块运动矢量平滑度先验进行时间去噪,从而消除不必要的时间闪烁。其次,提出了一种基于图的事件分类方案,以准确、稳健地检测呼吸暂停/低呼吸暂停。实验结果表明,基于图的时间去噪方案在去除闪烁方面优于时间中值滤波器的实现。其次,我们证明了我们基于图的事件分类方案对训练数据中的错误的鲁棒性明显优于两种传统的支持向量机(SVM)实现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信