Assessment of Artificial Intelligence Techniques for Automated Remote Classification of Cardiac Arrhythmia using Instantaneous Heart Rates

Prabodh Panindre, Mahotsavy Dama, Vijay Gandhi, Sunil Kumar

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

Abstract

Cardiac arrhythmia can cause serious health risks including sudden cardiac events, and a significant number of vulnerable individuals are undiagnosed or undertreated. Noting the widespread use of wireless health trackers and the efficacy of Artificial intelligence (AI) methods in processing a large amount of time-series sequential data, in this study we aim to find the best AI technique for diagnosing an arrhythmia. Various AI models have been trained and tested by utilizing publicly available medical data. From the confusion matrix, the accuracy, recall, F1-score, Area Under the Curve (AUC), and precision of each AI model have been evaluated and compared. This analysis and Friedman Tests indicate that Bi-LSTM-the deep learning method outperformed the classical machine learning methods. The process of remotely classifying arrhythmia provided in this study can be generalized for automatic diagnosis of many health risks.

Original languageEnglish (US)
Title of host publicationICAICST 2021 - 2021 International Conference on Artificial Intelligence and Computer Science Technology
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages25-30
Number of pages6
ISBN (Electronic)9781665424042
DOIs
StatePublished - Jun 29 2021
Event2021 International Conference on Artificial Intelligence and Computer Science Technology, ICAICST 2021 - Virtual, Online
Duration: Jun 29 2021 → …

Publication series

NameICAICST 2021 - 2021 International Conference on Artificial Intelligence and Computer Science Technology

Conference

Conference2021 International Conference on Artificial Intelligence and Computer Science Technology, ICAICST 2021
CityVirtual, Online
Period6/29/21 → …

Keywords

  • Arrhythmia
  • Deep Learning
  • Machine Learning
  • Tele-Health
  • Wireless Health Trackers

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Control and Optimization

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