Atrial fibrillation detection using machine learning algorithm from single lead electrocardiograms

Aim. Atrial fibrillation (AF) represents one of the most critical cardiac arrhythmias, as it significantly increases the risk of stroke. Its detection is particularly challenging due to the unpredictable nature of its episodes.

Methods. This study proposes a low-complexity algorithm, enabling integration into embedded devices for realtime AF episode detection. The proposed method integrates non-linear, time-domain and frequency-domain features extracted from electrocardiogram signals with The LightGBM algorithm (an extension of decision tree algorithm) is used to classify and detect AF.

Results. The model was trained using the MIT-BIH AF Database (MIT-AFDB), achieving sensitivity (Se), specificity (Sp), accuracy rates (Acc), precision (PPV), F1-score and AUC of 0.9838, 0.9690, 0.9748, 0.9543, 0.9688 and 0.9957, respectively. We also performed 10‑fold cross‑validation on this dataset. The obtained values for Se, Sp, Acc, PPV, F1-score, and AUC were, respectively, 0.9837 ± 0.0020, 0.9701 ± 0.0021, 0.9755 ± 0.0007, 0.9559 ± 0.0029, 0.9696 ± 0.0008, and 0.9959 ± 0.0002. This indicates that the model achieves good performance compared to current studies in AF recognition and detection.

Conclusions. The experimental results demonstrate that the model achieves high performance in the classification and detection of AF episodes. Furthermore, the model is suitable for integration into real-time arrhythmia detection systems.

1. Joglar JA, Chung MK, Armbruster AL et al. 2023 ACC/ AHA/ACCP/HRS guideline for the diagnosis and management of atrial fibrillation: a report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Journals of the American College of Cardiology. 2024;83(1): 109-279. https://doi.org/10.1016/j.jacc.2023.08.017.

2. Moody GB, Mark RG. A new method for detecting atrial fibrillation using RR intervals. Computer in Cardiology. 1983;10: 227-230.

3. Cerutti S, Mainardi LT, Porta A et al. Analysis of the dynamics of RR interval series for the detection of atrial fibrillation episodes. Computer in Cardiology. 1997: 77- 80. https://doi.org/10.1109/CIC.1997.647834.

4. Horie T, Burioka N, Amisaki T et al. Sample entropy in electrocardiogram during atrial fibrillation. Yonago Acta Medica. 2018;61(1): 049-057. https://doi.org/10.33160/yam.2018.03.007.

5. Slocum J, Sahakian A, Swiryn S. Diagnosis of atrial fibrillation from surface electrocardiograms based-on computer-detected atrial activity. Journal of Electrocardiology. 1992;25(1): 1-8. https://doi.org/10.1016/0022-0736(92)90123-H.

6. Hirsch G, Jensen SH, Poulsen ES et al. Atrial fibrillation detection using heart rate variability and atrial activity: A hybrid approach. Expert Systems with Applications. 2021;169: 114452. https://doi.org/10.1016/j.eswa.2020.114452.

7. Andersen RS, Peimankar A, Puthusserypady S. A deep learning approach for real-time detection of atrial fibrillation. Expert Systems with Applications. 2019;115: 465- 473. https://doi.org/10.1016/j.eswa.2018.08.011.

8. Petmezas G, Haris K, Stefanopoulos L et al. Automated atrial fibrillation detection using a hybrid CNN-LSTM network on imbalanced ECG datasets. Biomedical Signal Processing and Control. 2021;63: 102194. https://doi.org/10.1016/j.bspc.2020.102194.

9. Jun TJ, Nguyen HM, Kang D et al. ECG arrhythmia classification using a 2-D convolutional neural network. ArXiv preprint arXiv. 2018;arXiv1804.06812. https://doi.org/10.48550/arXiv.1804.06812.

10. Xu X, Wei S, Ma C et al. Atrial fibrillation beat identification using the combination of modified frequency slice wavelet transform and convolutional neural networks. Journal of Healthcare Engineering. 2018;1: 2102918. https://doi.org/10.1155/2018/2102918.

11. Nankani D, Baruah RD. Effective removal of baseline wander from ECG signals: A comparative study, in: Arup Bhattacharjee, Samir Kr. Borgohain, Badal Soni, Gyanendra Verma, Xiao-Zhi Gao (Eds). Machine Learning, Image Processing, Network Security and Data Sciences: Second International Conference, MIND 2020, Silchar, India, July30-31, 2020, Proceedings, Part II 2, Springer Singapore. 2020: 310-324.

12. Hargittai S. Savitzky-Golay least-squares polynomial filters in ECG signal processing. Computer in Cardiology. 2005: 763-766. https://doi.org/10.1109/CIC.2005.1588216.

13. Pan J, Tompkins WJ. A real-time QRS detection algorithm. IEEE Transactions on Biomedical Engineering. 1985;3: 230-236. https://doi.org/10.1109/TBME.1985.325532.

14. Zhang Q, Manriquez AI, Médigue C et al. An algorithm for robust and efficient location of T-wave ends in electrocardiograms. IEEE Transactions on Biomedical Engineering. 2006;53(12): 2544-2552. https://doi.org/10.1109/TBME.2006.884644.

15. Goldberger A, Amaral L, Glass L et al. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 2000;101(23): e215-e220.

16. Tsutsui K, Brimer SB, Ben-Moshe N et al. SHDBAF: a Japanese Holter ECG database of atrial fibrillation. ArXiv preprint arXiv. 2024 Jun;arXiv: 2406.16974. https://doi.org/10.48550/arXiv.2406.16974.

17. LightGBM. Welcome to LightGBM’s documentation, 2025. Accessed 5 March 2025 https://lightgbm.readthedocs.io/en/stable/

18. Frazier PI. A tutorial on Bayesian optimization. ArXiv preprint arXiv. 2018; arXiv: 1807.02811. https://doi.org/10.48550/arXiv.1807.02811.

19. Zou Y, Yu X, Li S et al. A generalizable and robust deep learning method for atrial fibrillation detection from long-term electrocardiogram. Biomedical Signal Processing and Control. 2024;90: 105797. https://doi.org/10.1016/j.bspc.2023.105797.

20. Du X, Rao N, Qian M et al. A novel method for real time atrial fibrillation detection in electrocardiograms using multiple parameters. Ann. Noninv. Electrocardiol. 2014;19(3): 217-225. https://doi.org/10.1111/anec.12111.

21. Rodenas J, Garcia M, Alcaraz R et al. Wavelet-entropy automatically detects episodes of atrial fibrillation from single lead electrocardiograms. Entropy. 2015;17(9): 6179-6199. https://doi.org/10.3390/e17096179.