Evaluating Deep Learning Algorithms for Real-Time Arrhythmia Detection

Tyler Petty, Thong Vu, Xinghui Zhao, Robert A. Hirsh, Greggory Murray, Francis M. Haas, Wei Xue

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

Abstract

Cardiovascular diseases, such as heart attack and congestive heart failure, are the leading cause of death both in the United States and worldwide. The current medical practice for diagnosing cardiovascular diseases is not suitable for long-term, out-of-hospital use. A key to long-term monitoring is the ability to detect abnormal cardiac rhythms, i.e., arrhythmia, in real-time. In this paper, we present our work in designing real-time sensing, and evaluating machine learning algorithms for real-time arrhythmia detection. Most of the existing work applies machine learning algorithms to electrocardiogram (ECG) images to detect abnormal patterns. These approaches are not suitable for real-time processing due to high processing overhead. In our work, we treat data as time series, and evaluate various machine learning algorithms in terms of both learning and computational performance. Our experimental results show that the long short-term memory network (LSTM) has both high accuracy and efficiency, demonstrating great potential for online detection of arrhythmia.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages19-26
Number of pages8
ISBN (Electronic)9780738123967
DOIs
StatePublished - Dec 2020
Event7th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2020 - Virtual, Leicester, United Kingdom
Duration: Dec 7 2020Dec 10 2020

Publication series

NameProceedings - 2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2020

Conference

Conference7th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2020
Country/TerritoryUnited Kingdom
CityVirtual, Leicester
Period12/7/2012/10/20

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems
  • Software
  • Information Systems and Management

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