Automatic Detection of Cardiac Arrhythmias Using Ensemble Learning

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

Abstract

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 2019 IEEE Region10 Conference
PublisherIEEE
Publication date2019
Pages381-86
ISBN (Print)978-1-7281-1895-6
Publication statusPublished - 2019
Event2019 IEEE Region10 Conference - Hotel Grand Hyatt Kochi, Kerala, India
Duration: 17 Oct 201920 Oct 2019
https://www.tencon2019.org/

Conference

Conference2019 IEEE Region10 Conference
LocationHotel Grand Hyatt Kochi
CountryIndia
CityKerala
Period17/10/201920/10/2019
Internet address

Keywords

  • Electrocardiogram (ECG)
  • Heartbeat classification
  • Ensemble learning
  • Feature extraction

Cite this

Peimankar, A., Jajroodi, M. J., & Puthusserypady, S. (2019). Automatic Detection of Cardiac Arrhythmias Using Ensemble Learning. In Proceedings of 2019 IEEE Region10 Conference (pp. 381-86). IEEE.