Comparison of Performance of Artificial Intelligence Algorithms for Real-Time Atrial Fibrillation Detection using Instantaneous Heart Rate

Prabodh Panindre, Vijay Gandhi, Sunil Kumar

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

Abstract

Atrial Fibrillation (AFib) is an abnormal heart rhythm (arrhythmia) condition that may cause a fatal cardioembolic stroke. The episode of AFib can be paroxysmal which increases challenges for its clinical manual diagnosis and affects the quality of life. Real-time cardiac monitoring with wearable health trackers can improve the chances of detecting this unpredictable event. In this paper, various Artificial Intelligence (AI) algorithms have been developed to classify beat-to-beat variation of AFib episodes in real-time using Instantaneous Heart Rates (IHR). Publicly-available clinical datasets from Physionet.org have been used for training and testing the AI algorithms. The accuracy, sensitivity, specificity, precision, F1 score, recall, and area under the receiver operating characteristic curve of these algorithms are evaluated and compared. It was found that, in comparison to other AI algorithms, the deep Recurrent Neural Network (RNN) with Bi-directional Long Short-Term Memory (LSTM) demonstrates better performance for classifying the AFib episodes. The models developed can be integrated into wireless health tracker-based mHealth applications to detect AFib using IHR in real-time.

Original languageEnglish (US)
Title of host publicationHONET 2020 - IEEE 17th International Conference on Smart Communities
Subtitle of host publicationImproving Quality of Life using ICT, IoT and AI
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages168-172
Number of pages5
ISBN (Electronic)9780738105277
DOIs
StatePublished - Dec 14 2020
Event17th IEEE International Conference on Smart Communities: Improving Quality of Life using ICT, IoT and AI, HONET 2020 - Virtual, Charlotte, United States
Duration: Dec 14 2020Dec 16 2020

Publication series

NameHONET 2020 - IEEE 17th International Conference on Smart Communities: Improving Quality of Life using ICT, IoT and AI

Conference

Conference17th IEEE International Conference on Smart Communities: Improving Quality of Life using ICT, IoT and AI, HONET 2020
CountryUnited States
CityVirtual, Charlotte
Period12/14/2012/16/20

Keywords

  • Artificial Intelligence
  • Atrial Fibrillation
  • Deep Learning
  • mHealth
  • Wireless health Trackers

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Information Systems and Management
  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment

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