您好，欢迎访问中国农业科学院 机构知识库！

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

Automatic Silence Events Detector from Smartphone Audio Signals: A Pilot mHealth System for Sleep Apnea Monitoring at Home

文献类型： 会议论文

第一作者： Yolanda Castillo-Escario

作者： Yolanda Castillo-Escario 1 ; Ignasi Ferrer-Lluis 1 ; Josep Maria Montserrat 2 ; Raimon Jané 1 ;

作者机构： 1.Institute for Bioengineering of Catalonia (IBEC), the Barcelona Institute of Science and Technology (BIST), Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) and ESAII Department, Universitat Politècnica de Catalunya - BarcelonaTech (UPC), Barcelona, Spain

2.Hospital Clínic de Barcelona (Sleep Lab, Pneumology Service) and Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES)

关键词： OSA;prevalent disease;personalized

会议名称： International Conference of the IEEE Engineering in Medicine and Biology Society

主办单位：

页码： 4982-4985

摘要： Obstructive sleep apnea (OSA) is a prevalent disease, but most patients remain undiagnosed and untreated. Recently, mHealth tools are being proposed to screen OSA patients at home. In this work, we analyzed full-night audio signals recorded with a smartphone microphone. Our objective was to develop an automatic detector to identify silence events (apneas or hypopneas) and compare its performance to a commercial portable system for OSA diagnosis (ApneaLink?, ResMed). To do that, we acquired signals from three subjects with both systems simultaneously. A sleep specialist marked the events on smartphone and ApneaLink signals. The automatic detector we developed, based on the sample entropy, identified silence events similarly than manual annotation. Compared to ApneaLink, it was very sensitive to apneas (detecting 86.2%) and presented an 83.4% positive predictive value, but it missed about half the hypopnea episodes. This suggests that during some hypopneas the flow reduction is not reflected in sound. Nevertheless, our detector accurately recognizes silence events, which can provide valuable respiratory information related to the disease. These preliminary results show that mHealth devices and simple microphones are promising non-invasive tools for personalized sleep disorders management at home.

分类号： r318-532

  • 相关文献

[1]Modeling Sleep Apnea Severity Using Bioimpedance Measurements. Bojan Gavrilovic,Milos. R Popovic,Azadeh Yadollahi. 2015

[2]Cardiorespiratory Phase Synchronization in OSA subjects during wake and sleep states. Jordi Solà-Soler,Beatriz F. Giraldo,José A. Fiz,Raimon Jané. 2015

[3]Automatic Event Detector from Smartphone Accelerometry: Pilot mHealth Study for Obstructive Sleep Apnea Monitoring at Home. Yolanda Castillo-Escario,Josep Maria Montserrat,Raimon Jané,Ignasi Ferrer-Lluis. 2019

[4]Modeling Sleep Apnea Severity Using Bioimpedance Measurements. Gavrilovic, Bojan,Popovic, Milos R.,Yadollahi, Azadeh. 2015

作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：中国农业科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台