A Big-Data Approach to Defining Breathing Signatures for Identifying Respiratory Disease

被引：0

作者：

Rahman, Abrar ^{[1
]}

Weiner, Yonathan ^{[2
]}

Swanson, Hailey ^{[3
]}

Slepian, Rebecca ^{[3
]}

Abdullah, Anusheh ^{[4
]}

Slepian, Marvin J. ^{[3
]}

机构：

[1] Univ Calif Berkeley, 101 Durant Hall, Berkeley, CA 94720 USA

[2] Worcester Polytech Inst WPI, 100 Inst Rd, Worcester, MA 01609 USA

[3] Univ Arizona, 1501 N Campbell Ave, Tucson, AZ 85724 USA

[4] Univ Calif Davis, 1 Shields Ave, Davis, CA 95616 USA

来源：

2019 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) | 2019年

关键词：

Big-data; breathing signatures; pattern mining; respirations; Forced Vital Capacity; lung performance; data modeling; classification;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This project seeks to use wearable sensors to develop a novel method for measuring respiratory activity in human subjects. This is the first stage of an ongoing project under the Arizona Center for Accelerated Biomedical Innovation (ACABI) [1]. The ultimate ambition of this effort is to develop a baseline digital breathing signature for a particular individual, so that medical professionals equipped with big-data analysis tools can use deviations from one's signature to differentiate between conventional breathing and abnormal breathing patterns, such as splinting and Kussmaul respirations.

引用

页码：6195 / 6197

页数：3

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