Comparative Study on Heart Rate Variability Analysis for Atrial Fibrillation Detection in Short Single-Lead ECG Recordings
Nguyen A, Ansari S, Hooshmand M, Lin K, Ghanbari H, Gryak J, Najarian K (2018)
Publication Type: Conference contribution
Publication year: 2018
Journal
Publisher: Institute of Electrical and Electronics Engineers Inc.
Book Volume: 2018-July
Pages Range: 526-529
Conference Proceedings Title: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Event location: Honolulu, HI
ISBN: 9781538636466
DOI: 10.1109/EMBC.2018.8512345
Abstract
Detection of atrial fibrillation (AFib) using wearable ECG monitors has recently gained popularity. The signal quality of such recordings is often much lower than that of traditional monitoring systems such as Holter monitors. Larger noise contamination can lead to reduced accuracy of the QRS detection which is the basis of the heart rate variability (HRV) analysis. Hence, it is crucial to accurately classify short ECG recording segments for AFib monitoring. A comparative study was conducted to investigate the applicability and performance of a variety of HRV feature extraction methods applied to short single lead ECG recordings to detect AFib. The data employed in this study is the publicly available dataset of the 2017 PhysioNet challenge. In particular, detection of AFib against non-AFib instances, including normal sinus rhythm, other types of arrhythmias and noisy signals, is investigated in this study. The HRV features can be divided into the categories of statistical, geometrical, frequency, entropy, Poincare plotand Lorentz plot-based. For feature selection, stepwise forward selection approach was employed and support vector machines with linear and radial basis function kernels were used for classification. The results indicate that a combination of features from all the categories leads to the highest accuracy levels. The feasibility of using different HRV features for short signals is discussed as well. In conclusion, AFib can be detected with high accuracy using short single-lead ECG signals using HRV features.
Authors with CRIS profile
Involved external institutions
How to cite
APA:
Nguyen, A., Ansari, S., Hooshmand, M., Lin, K., Ghanbari, H., Gryak, J., & Najarian, K. (2018). Comparative Study on Heart Rate Variability Analysis for Atrial Fibrillation Detection in Short Single-Lead ECG Recordings. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (pp. 526-529). Honolulu, HI, US: Institute of Electrical and Electronics Engineers Inc..
MLA:
Nguyen, An, et al. "Comparative Study on Heart Rate Variability Analysis for Atrial Fibrillation Detection in Short Single-Lead ECG Recordings." Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018, Honolulu, HI Institute of Electrical and Electronics Engineers Inc., 2018. 526-529.
BibTeX: Download