Detection of Atrial Fibrillation with Custom Designed Wavelet-based Convolutional Autoencoder

Abstract

Remote monitoring of patients is of great importance in terms of early diagnosis of diseases and improving people's quality of life. With the rapid development of deep learning techniques, wearable health technologies have leaped forward. This has made the automatic diagnosis even more important. In this study, we provide a deep learning approach for classifying Atrial Fibrillation (AF) arrhythmia that uses a customized wavelet-based convolutional autoencoder (WCAE) model. WCAE is employed as an anomaly detector, which combines the time-frequency domain examination ability of wavelet and the data-driven feature learning capability of convolutional autoencoders. The proposed approach received average scores of 95.45%, 99.99%, 90.90%, and 95.23% for accuracy, precision, recall, and F1, respectively, on a large selection of publicly available datasets. The outcomes of the experiments demonstrate the significance of using deep learning-based models in diagnosing AF. Moreover, it is observed that utilization of wavelet methods along with autoencoder model has a great potential for biomedical signal processing systems.

Keywords

• Atrial fibrillation detection
• ECG
• Autoencoder
• Deep learning
• Discrete wavelet transform

Supporting Institution

This study has been supported by the The Scientific and Technological Research Council of Turkiye TÜBİTAK 1001-121E119 Research Project.

Project Number

Tübitak 121E119

References

1. [1]. Sagris, M., Vardas, E. P., Theofilis, P., Antonopoulos, A. S., Oikonomou, E., & Tousoulis, D. (2021). Atrial fibrillation: pathogenesis, predisposing factors, and genetics. International journal of molecular sciences, 23(1), 6.
2. [2]. Clinical Practice Guidelines 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 Developed in Collaboration With and Endorsed by the American College of Clinical Pharmacy and the Heart Rhythm Society, PMID: 38153996 DOI: 10.1161/CIR.0000000000001207
3. [3]. Siontis, K.C., Noseworthy, P.A., Attia, Z.I. et al. Artificial intelligence-enhanced electrocardiography in cardiovascular disease management. Nat Rev Cardiol 18, 465–478 (2021). https://doi.org/10.1038/s41569-020-00503-2
4. [4]. Filos, D., Tachmatzidis, D., Maglaveras, N., Vassilikos, V., & Chouvarda, I. (2019). Understanding the beat-to-beat variations of P-waves morphologies in AF patients during sinus rhythm: a scoping review of the atrial simulation studies. Frontiers in Physiology, 10, 742.
5. [5]. Chung, E. K. (2013). Ambulatory electrocardiography: holter monitor electrocardiography. Springer Science & Business Media.
6. [6]. Wijesurendra, R. S., & Casadei, B. (2019). Mechanisms of atrial fibrillation. Heart, 105(24), 1860-1867.
7. [7]. Hu, Y., Zhao, Y., Liu, J., Pang, J., Zhang, C., & Li, P. (2020). An effective frequency-domain feature of atrial fibrillation based on time–frequency analysis. BMC Medical Informatics and Decision Making, 20, 1-11.
8. [8]. Chen, Y., Zhang, C., Liu, C., Wang, Y., & Wan, X. (2022). Atrial fibrillation detection using a feedforward neural network. Journal of Medical and Biological Engineering, 42(1), 63-73.

9. [9]. Cheng, Y., Hu, Y., Hou, M., Pan, T., He, W., & Ye, Y. (2020). Atrial fibrillation detection directly from compressed ECG with the prior of measurement matrix. Information, 11(9), 436.
10. [10]. Wei, T. R., Lu, S., & Yan, Y. (2022). Automated atrial fibrillation detection with ECG. Bioengineering, 9(10), 523.
11. [11]. Faust, O., Shenfield, A., Kareem, M., San, T. R., Fujita, H., & Acharya, U. R. (2018). Automated detection of atrial fibrillation using long short-term memory network with RR interval signals. Computers in biology and medicine, 102, 327-335.
12. [12]. Rasmussen, S. M., Jensen, M. E., Meyhoff, C. S., Aasvang, E. K., & Słrensen, H. B. (2021, November). Semi-supervised analysis of the electrocardiogram using deep generative models. In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 1124-1127). IEEE.
13. [13]. Bank, D., Koenigstein, N., & Giryes, R. (2023). Autoencoders. Machine learning for data science handbook: data mining and knowledge discovery handbook, 353-374.
14. [14]. Singh, A., & Ogunfunmi, T. (2021). An overview of variational autoencoders for source separation, finance, and bio-signal applications. Entropy, 24(1), 55.
15. [15]. Ojha, M. K., Wadhwani, S., Wadhwani, A. K., & Shukla, A. (2022). Automatic detection of arrhythmias from an ECG signal using an auto-encoder and SVM classifier. Physical and engineering sciences in medicine, 45(2), 665-674.
16. [16]. Choi, S., Choi, K., Yun, H. K., Kim, S. H., Choi, H. H., Park, Y. S., & Joo, S. (2024). Diagnosis of atrial fibrillation based on AI-detected anomalies of ECG segments. Heliyon, 10(1).
17. [17]. Eravcı, Ö., Özkurt, N., "Arrhythmia Detection with Custom Designed Wavelet-based Convolutional Autoencoder," 2023 International Conference on Innovations in Intelligent Systems and Applications (INISTA’2023), Hammamet, Tunisia, 2023, pp. 1-5, https://doi.org/10.1109/INISTA59065.2023.10310328
18. [18]. Isabels, K.R., Devi, K.M., Anand, R., Athe, R., Chowdhury, S.S., Pund, S.S., “An Intellectual Fusion Classification Prototypical for an Imbalanced Electrocardiogram Data”, SN Computer Science (2023) 4:721, https://doi.org/10.1007/s42979-023-02120-5
19. [19]. Shaik, J., Bhavanam, S.N, “Arrhythmia Detection Using ECG‑Based Classification with Prioritized Feature Subset Vector‑Associated Generative Adversarial Network”, SN Computer Science (2023) 4:519, https://doi.org/10.1007/s42979-023-01970-3
20. [20]. A.L. Goldberger, L.A.N. Amaral, L. Glass, et al., PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals [J], Circulation 101 (23) (2000) e215–e220, https://doi.org/10.1161/01.cir.101.23.e215
21. [21]. G. Moody, A new method for detecting atrial fibrillation using RR intervals[J], Comput. Cardiol. (1983) 227–230.
22. [22]. Clifford, G. D., Liu, C., Moody, B., Li-wei, H. L., Silva, I., Li, Q. & Mark, R. G. (2017, September). AF classification from a short single lead ECG recording: The PhysioNet/computing in cardiology challenge 2017. In 2017 Computing in Cardiology (CinC) (pp. 1-4). IEEE.
23. [23]. Versaci, F. (2020). WaveTF: A Fast 2D Wavelet Transform for Machine Learning in Keras. ICPR Workshops.
24. [24]. Addison, P.S., The Illustrated Wavelet Transform Handbook: Introductory Theory and Applications in Science, Engineering, Medicine and Finance, CRC Press, 2002