Arrhythmia Classification on Electrocardiogram Signal Using Convolution Neural Network Based on Frequency Spectrum

Arief Kurniawan, Ananda, Firdaus Nanda Pradanggapasti, Reza Fuad Rachmadi, Eko Setijadi, Eko Mulyanto Yuniarno, Mochamad Yusuf, I. Ketut Eddy Purnama

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

4 Citations (Scopus)

Abstract

Heart disease is the leading cause of death in the world. To find out heart disease early, it can be detected by examining the presence or absence of arrhythmias. Arrhythmia is an abnormal heart beat rhythm, can beat too fast, too slow, or beat with irregular patterns, so that the arrhythmia has many types. To diagnose arrhythmias, one method that can be used is by analyzing ECG (Electrocardiogram) signals. Currently, doctors and medical personnel analyze ECG signals manually. Because the number of cardiologist paramedics is far less than the number of patients, patients need hardware or software to analyze the heart independently. With the development of technology in this era, there is a technology called Deep Learning. Deep Learning is a development of Machine Learning. In this paper, we proposed one method of Deep Learning, namely Convolutional Neural Network (CNN), is used to classify 5 types of arrhythmias on ECG signals, that are: Normal Beat (NOR), Right Bundle Branch Block (RBBB), Left Bundle Branch Block (LBBB), Premature Ventricular Contraction (PVC) and Fusion of Ventricular and Normal (FVN). Evaluating the performance of our CNN architecture, we tested it to classify the heart beat in the MIT BIH Arrhythmia database. The performance results of our proposed have highest accuracy is 98.6% with the following details: 713 Normal Beat samples can be detected correctly (100%), RBBB 689 samples (96.63%), LBBB 710 samples (99.58%), FVN 713 samples (100%), and PVC 690 samples (96.77%).

Original languageEnglish
Title of host publicationCENIM 2020 - Proceeding
Subtitle of host publicationInternational Conference on Computer Engineering, Network, and Intelligent Multimedia 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages29-33
Number of pages5
ISBN (Electronic)9781728182834
DOIs
Publication statusPublished - 17 Nov 2020
Externally publishedYes
Event2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2020 - Virtual, Surabaya, Indonesia
Duration: 17 Nov 202018 Nov 2020

Publication series

NameCENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020

Conference

Conference2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2020
Country/TerritoryIndonesia
CityVirtual, Surabaya
Period17/11/2018/11/20

Keywords

  • Arrhythmia
  • CNN
  • ECG
  • Heartbeat classification
  • Spectogram

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