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

Abrar Rahman, Yonathan Weiner, Hailey Swanson, Rebecca Slepian, Anusheh Abdullah, Marvin J. Slepian

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE International Conference on Big Data, Big Data 2019
EditorsChaitanya Baru, Jun Huan, Latifur Khan, Xiaohua Tony Hu, Ronay Ak, Yuanyuan Tian, Roger Barga, Carlo Zaniolo, Kisung Lee, Yanfang Fanny Ye
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6195-6197
Number of pages3
ISBN (Electronic)9781728108582
DOIs
StatePublished - Dec 2019
Event2019 IEEE International Conference on Big Data, Big Data 2019 - Los Angeles, United States
Duration: Dec 9 2019Dec 12 2019

Publication series

NameProceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

Conference

Conference2019 IEEE International Conference on Big Data, Big Data 2019
CountryUnited States
CityLos Angeles
Period12/9/1912/12/19

Keywords

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

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management

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  • Cite this

    Rahman, A., Weiner, Y., Swanson, H., Slepian, R., Abdullah, A., & Slepian, M. J. (2019). A Big-Data Approach to Defining Breathing Signatures for Identifying Respiratory Disease. In C. Baru, J. Huan, L. Khan, X. T. Hu, R. Ak, Y. Tian, R. Barga, C. Zaniolo, K. Lee, & Y. F. Ye (Eds.), Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019 (pp. 6195-6197). [9006124] (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData47090.2019.9006124