A scalable automated diagnostic feature extraction system for EEGs

Prakhar Agrawal, Divya Bhargavi, Gokul Krishna, Xiao Han, Neha Tevathia, Abbie M. Popa, Nicholas Ross, Diane Myung kyung Woodbridge, Barbie Zimmerman-Bier, William J. Bosl

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

Abstract

Researchers using Electroencephalograms (“EEGs”) to diagnose clinical outcomes often run into computational complexity problems. In particular, extracting complex, sometimes nonlinear, features from a large number of time-series often require large amounts of processing time. In this paper we describe a distributed system that leverages modern cloud-based technologies and tools and demonstrate that it can effectively, and efficiently, undertake clinical research. Specifically we compare three types of clusters, showing their relative costs (in both time and money) to develop a distributed machine learning pipeline for predicting gestation time based on features extracted from these EEGs.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE 43rd Annual Computer Software and Applications Conference, COMPSAC 2019
EditorsVladimir Getov, Jean-Luc Gaudiot, Nariyoshi Yamai, Stelvio Cimato, Morris Chang, Yuuichi Teranishi, Ji-Jiang Yang, Hong Va Leong, Hossian Shahriar, Michiharu Takemoto, Dave Towey, Hiroki Takakura, Atilla Elci, Susumu Takeuchi, Satish Puri
PublisherIEEE Computer Society
Pages446-451
Number of pages6
ISBN (Electronic)9781728126074
DOIs
StatePublished - Jul 2019
Event43rd IEEE Annual Computer Software and Applications Conference, COMPSAC 2019 - Milwaukee, United States
Duration: Jul 15 2019Jul 19 2019

Publication series

NameProceedings - International Computer Software and Applications Conference
Volume2

Conference

Conference43rd IEEE Annual Computer Software and Applications Conference, COMPSAC 2019
CountryUnited States
CityMilwaukee
Period7/15/197/19/19

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications

Keywords

  • Cloud Computing
  • Distributed Database
  • Distributed Processing
  • EEG
  • Electroencephalography
  • Machine Learning
  • NoSQL

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  • Cite this

    Agrawal, P., Bhargavi, D., Krishna, G., Han, X., Tevathia, N., Popa, A. M., Ross, N., Woodbridge, D. M. K., Zimmerman-Bier, B., & Bosl, W. J. (2019). A scalable automated diagnostic feature extraction system for EEGs. In V. Getov, J-L. Gaudiot, N. Yamai, S. Cimato, M. Chang, Y. Teranishi, J-J. Yang, H. V. Leong, H. Shahriar, M. Takemoto, D. Towey, H. Takakura, A. Elci, S. Takeuchi, & S. Puri (Eds.), Proceedings - 2019 IEEE 43rd Annual Computer Software and Applications Conference, COMPSAC 2019 (pp. 446-451). [8754005] (Proceedings - International Computer Software and Applications Conference; Vol. 2). IEEE Computer Society. https://doi.org/10.1109/COMPSAC.2019.10247