Deep Learning-Based Arrhythmia Detection Using RR-Interval Framed Electrocardiograms

Song Kyoo Kim, Chan Yeob Yeun, Paul D. Yoo, Nai Wei Lo, Ernesto Damiani

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Deep learning applied to electrocardiogram (ECG) data can be used to achieve personal authentication in biometric security applications, but it has not been widely used to diagnose cardiovascular disorders. We developed a deep learning model for the detection of arrhythmia in which time-sliced ECG data representing the distance between successive R-peaks are used as the input for a convolutional neural network (CNN). The main objective is developing the compact deep learning-based detect system which minimally uses the dataset but delivers the confident accuracy rate of the arrhythmia detection. This compact system can be implemented in wearable devices or real-time monitoring equipment because the feature extraction step is not required for complex ECG waveforms, only the R-peak data is needed. The 10 hidden layers of the CNN detect arrhythmias using a novel RR-interval framing (RRIF) approach. Two testing processes were implemented, the first during the training and validation of the CNN algorithm and the second using different datasets for testing under realistic conditions. The results of both tests indicated that the Compact Arrhythmia Detection System (CADS) matched the performance of conventional systems for the detection of arrhythmia in two consecutive test runs.

Original languageEnglish
Title of host publicationProceedings of 8th International Congress on Information and Communication Technology - ICICT 2023
EditorsXin-She Yang, R. Simon Sherratt, Nilanjan Dey, Amit Joshi
PublisherSpringer Science and Business Media Deutschland GmbH
Pages11-21
Number of pages11
ISBN (Print)9789819930906
DOIs
Publication statusPublished - 2023
Event8th International Congress on Information and Communication Technology, ICICT 2023 - London, United Kingdom
Duration: 20 Feb 202323 Feb 2023

Publication series

NameLecture Notes in Networks and Systems
Volume694 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference8th International Congress on Information and Communication Technology, ICICT 2023
Country/TerritoryUnited Kingdom
CityLondon
Period20/02/2323/02/23

Keywords

  • Arrhythmia
  • Convolutional neural network
  • Deep learning
  • Electrocardiogram
  • MATLAB
  • Time-sliced data

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