Atrial Fibrillation Detection Based on EEMD and XGBoost

The electrocardiogram (ECG) is non-invasive, inexpensive and widely used in several applications, implemented to detect the physical condition and disease of the human body. Atrial fibrillation (AF) is the most common of many different forms of sustained arrhythmia. Therefore, early diagnosis of AF...

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Bibliographic Details
Published in:Journal of physics. Conference series 2019-05, Vol.1229 (1), p.12074
Main Authors: Yue, Zhang, Jinjing, Zhu
Format: Article
Language:English
Subjects:
Arrhythmia
Cardiac arrhythmia
Deep learning
Electrocardiography
Fibrillation
Heart diseases
Machine learning
Object recognition
Physics
Rhythm
Speech recognition
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Summary:The electrocardiogram (ECG) is non-invasive, inexpensive and widely used in several applications, implemented to detect the physical condition and disease of the human body. Atrial fibrillation (AF) is the most common of many different forms of sustained arrhythmia. Therefore, early diagnosis of AF may help to improve doctor's diagnostic efficiency and is essential to prevent further progression of Atrial fibrillation to other heart disease and stroke complications. With the popularity of the machine learning and deep learning, more and more researchers apply them in image recognition, speech recognition and so on. Naturally, there are also many studies which achieve the purpose of diagnosing diseases, such as detection of arrhythmia, biometric identification based on ECG signals and machine learning or deep learning. A novel approach to detect AF from ECG signals was developed on this study, we used great filter EEMD (Ensemble Empirical Mode Decomposition) and classifier XGBoost (eXtreme Gradient Boosting) to detect normal rhythm, AF and other rhythm. Finally, the great performance was achieved with an average F1 score of 0.84 and accuracy of 0.86.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1229/1/012074