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.
|Title of host publication||Proceedings of the TENCON 2019 : Technology, Knowledge, and Society|
|Publication date||Oct 2019|
|Publication status||Published - Oct 2019|
|Event||2019 IEEE Region 10 Conference: Technology, Knowledge, and Society, TENCON 2019 - Kerala, India|
Duration: 17. Oct 2019 → 20. Oct 2019
|Conference||2019 IEEE Region 10 Conference: Technology, Knowledge, and Society, TENCON 2019|
|Period||17/10/2019 → 20/10/2019|
|Sponsor||Cochin Shipyard Limited, et al., Kerala State - IT Mission, Nest, Nissan Digital, Terumo Penpol|
|Series||IEEE Region 10 Annual International Conference, Proceedings/TENCON|
Bibliographical noteFunding Information:
*Both authors contributed equally to this work. This research work is supported by the Innovation fund, Denmark, REAFEL (IFD Project No: 6153-00009B).
- electrocardiogram (ECG)
- ensemble learning
- feature extraction
- heartbeat classification