Automatic Detection of Cardiac Arrhythmias Using Ensemble Learning

Abdolrahman Peimankar, Mona Jafar Jajroodi, Sadasivan Puthusserypady

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Objectives: Electrocardiogram (ECG) is an important clinical tool for diagnosis of cardiac abnormalities. Physicians make diagnoses by visual examination of ECGs. Analysing huge amounts of ECGs however, can be very time consuming and cumbersome. Hence, developing analytic software is of great importance to automatically analyse these ECG signals to detect efficiently the common cardiac arrhythmias. Methods: Proposed an ensemble learning approach for automatic processing of ECG signals and classification of arrhythmias. Twenty six features (based on wavelets, heartbeat intervals, and RR-intervals) are extracted and three algorithms, namely, Random Forest (RF), Adaptive Boosting (AdaBoost) and Artificial Neural Network (ANN) are utilized for classification. Results: The proposed method is evaluated on ECG signals from 44 recordings of the MIT-BIH arrhythmia database. The overall classification accuracy of the RF, AdaBoost, and ANN are 96.16%, 96.16% and 94.49%, respectively. Additionally, the overall classification accuracy of the ensemble model is improved to 96.18%. Conclusion: Experimental results show that the performance of the ensemble model for ECG heartbeat classification improves the overall accuracy. Significance: This paper proposes an accurate and easy to use approach to classify heartbeats into one of the five classes recommended by ANSI/AAMI standard, which can be used in real-time within a tele-health monitoring framework.

Original languageEnglish
Title of host publicationProceedings of the TENCON 2019 : Technology, Knowledge, and Society
PublisherIEEE
Publication dateOct 2019
Pages383-388
Article number8929348
ISBN (Electronic)9781728118956
DOIs
Publication statusPublished - Oct 2019
Externally publishedYes
Event2019 IEEE Region 10 Conference: Technology, Knowledge, and Society, TENCON 2019 - Kerala, India
Duration: 17. Oct 201920. Oct 2019

Conference

Conference2019 IEEE Region 10 Conference: Technology, Knowledge, and Society, TENCON 2019
CountryIndia
CityKerala
Period17/10/201920/10/2019
SponsorCochin Shipyard Limited, et al., Kerala State - IT Mission, Nest, Nissan Digital, Terumo Penpol
SeriesIEEE Region 10 Annual International Conference, Proceedings/TENCON
Volume2019-October
ISSN2159-3442

Bibliographical note

Funding Information:
*Both authors contributed equally to this work. This research work is supported by the Innovation fund, Denmark, REAFEL (IFD Project No: 6153-00009B).

Keywords

  • electrocardiogram (ECG)
  • ensemble learning
  • feature extraction
  • heartbeat classification

Fingerprint

Dive into the research topics of 'Automatic Detection of Cardiac Arrhythmias Using Ensemble Learning'. Together they form a unique fingerprint.

Cite this