@inproceedings{6393c22ddac6413c8a5ca10013b4c84c,
title = "A Big-Data Approach to Defining Breathing Signatures for Identifying Respiratory Disease",
abstract = "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.",
keywords = "Big-data, Forced Vital Capacity, breathing signatures, classification, data modeling, lung performance, pattern mining, respirations",
author = "Abrar Rahman and Yonathan Weiner and Hailey Swanson and Rebecca Slepian and Anusheh Abdullah and Slepian, {Marvin J.}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Big Data, Big Data 2019 ; Conference date: 09-12-2019 Through 12-12-2019",
year = "2019",
month = dec,
doi = "10.1109/BigData47090.2019.9006124",
language = "English (US)",
series = "Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "6195--6197",
editor = "Chaitanya Baru and Jun Huan and Latifur Khan and Hu, {Xiaohua Tony} and Ronay Ak and Yuanyuan Tian and Roger Barga and Carlo Zaniolo and Kisung Lee and Ye, {Yanfang Fanny}",
booktitle = "Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019",
}