ENSEMBLE METHODS FOR ECG-BASED HEARTBEAT CLASSIFICATION

Published 30 APR 2019 •  vol 12  •  no 4  • 


Authors:

 

Rui Duan, Lakehead University, Canada
Sabah Mohammed, Lakehead University, Canada
Jinan Fiaidhi, Lakehead University, Canada

Abstract:

 

Ensemble method is a meta-algorithm to build strong classifiers based on a set of weak classifiers. This work explores some ensemble classifiers on UCI Arrhythmia Dataset to classify the heartbeat records. Each record corresponds to a person, and is the features extracted from the person’s raw ECG data over a period. Two popular ensemble classifiers XGBoost and RandomForest are used, and the classic LogisticRegression is tried as a comparison. These classifiers are applied on all the 279 features in the dataset, and predict the heartbeat categories for the records. XGBoost and RandomForest perform better than even well-tuned LogisticRegression. We build VotingClassifier based on the ensemble voting meta-algorithm and the above three built-in classifiers, and it outperforms even well-tuned XGBoost and RandomForest. The best prediction accuracy 76% is achieved by the VotingClassifier in this multiclass classification problem. This result is comparable to many other findings that uses similar or different classifiers.

Keywords:

 

ECG classification, XGBoost, Random Forest, Logistic Regression, Ensemble Method, Scikit-learn

References:

 

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Citations:

 

APA:
Duan, R., Mohammed, S., & Fiaidhi, J. (2019). Ensemble Methods for ECG-Based Heartbeat Classification. International Journal of Control and Automation (IJCA), ISSN: 2005-4297 (Print); 2207-6387 (Online), NADIA, 12(4), 29-46. doi: 10.33832/ijca.2019.12.4.03.

MLA:
Duan, Rui, et al. “Ensemble Methods for ECG-Based Heartbeat Classification.” International Journal of Control and Automation, ISSN: 2005-4297 (Print); 2207-6387 (Online), NADIA, vol. 12, no. 4, 2019, pp. 29-46. IJCA, http://article.nadiapub.com/IJCA/vol12_no4/3.html.

IEEE:
[1] R. Duan, S. Mohammed, and J. Fiaidhi, "Ensemble Methods for ECG-Based Heartbeat Classification." International Journal of Control and Automation (IJCA), ISSN: 2005-4297 (Print); 2207-6387 (Online), NADIA, vol. 12, no. 4, pp. 29-46, Apr 2019.