Prediction method of paroxysmal atrial fibrillation based on multimodal feature fusion_Journal of Biomedical Engineering

Authors：

LI Yongjian , LIU Lei , CHEN Meng , LI Yixue , WANG Yuchen ,  WEI Shoushui

School of Control Science and Engineering, Shandong University, Jinan 250061, P. R. China;

Corresponding author：

WEI Shoushui, Email: sswei@sdu.edu.cn

Keywords：

Atrial fibrillation prediction; Multimodal fusion; Feature selection; Multi-center data

DOI：

10.7507/1001-5515.202403039

Video：

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The risk prediction of paroxysmal atrial fibrillation (PAF) is a challenge in the field of biomedical engineering. This study integrated the advantages of machine learning feature engineering and end-to-end modeling of deep learning to propose a PAF risk prediction method based on multimodal feature fusion. Additionally, the study utilized four different feature selection methods and Pearson correlation analysis to determine the optimal multimodal feature set, and employed random forest for PAF risk assessment. The proposed method achieved accuracy of (92.3 ± 2.1)% and F1 score of (91.6 ± 2.9)% in a public dataset. In a clinical dataset, it achieved accuracy of (91.4 ± 2.0)% and F1 score of (90.8 ± 2.4)%. The method demonstrates generalization across multi-center datasets and holds promising clinical application prospects.

Citation： LI Yongjian, LIU Lei, CHEN Meng, LI Yixue, WANG Yuchen, WEI Shoushui. Prediction method of paroxysmal atrial fibrillation based on multimodal feature fusion. Journal of Biomedical Engineering, 2025, 42(1): 42-48. doi: 10.7507/1001-5515.202403039 Copy

1.	Camm A J, Naccarelli G V, Mittal S, et al. The increasing role of rhythm control in patients with atrial fibrillation: JACC state-of-the-art review. J Am Coll Cardiol, 2022, 79(19): 1932-1948.
2.	Guo Y, Wang H, Zhang H, et al. Mobile photoplethysmographic technology to detect atrial fibrillation. J Am Coll Cardiol, 2019, 74(19): 2365-2375.
3.	Kuniss M, Pavlovic N, Velagic V, et al. Cryoballoon ablation vs. antiarrhythmic drugs: first-line therapy for patients with paroxysmal atrial fibrillation. EP Europace, 2021, 23(7): 1033-1041.
4.	Al-Khatib S M, Wilkinson W E, Sanders L L, et al. Observations on the transition from intermittent to permanent atrial fibrillation. Am Heart J, 2000, 140(1): 142-145.
5.	Schnabel R B, Yin X, Gona P, et al. 50 year trends in atrial fibrillation prevalence, incidence, risk factors, and mortality in the Framingham Heart Study: a cohort study. Lancet, 2015, 386(9989): 154-162.
6.	German D M, Kabir M M, Dewland T A, et al. Atrial fibrillation predictors: importance of the electrocardiogram. Ann Noninvas Electro, 2016, 21(1): 20-29.
7.	Pipilas D, Friedman S F, Khurshid S. The use of artificial intelligence to predict the development of atrial fibrillation. Curr Cardiol Rep, 2023, 25(5): 381-389.
8.	Moody G B, Goldberger A L, Mcclennen S, et al. Predicting the onset of paroxysmal atrial fibrillation: the Computers in Cardiology Challenge 2001// Computers in Cardiology 2001. Rotterdam: IEEE, 2001: 113-116.
9.	Zong W, Mukkamala R, Mark R G. A methodology for predicting paroxysmal atrial fibrillation based on ECG arrhythmia feature analysis// Computers in Cardiology 2001. Rotterdam: IEEE, 2001: 125-128.
10.	Thong T, McNames J, Aboy M, et al. Prediction of paroxysmal atrial fibrillation by analysis of atrial premature complexes. IEEE T Bio-Med Eng, 2004, 51(4): 561-569.
11.	Thong T, McNames J, Aboy M, et al. Paroxysmal atrial fibrillation prediction using isolated premature atrial events and paroxysmal atrial tachycardia// Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Cancun: IEEE, 2003: 163-166.
12.	Hickey B, Heneghan C. Screening for paroxysmal atrial fibrillation using atrial premature contractions and spectral measures// Computers in Cardiology 2002. Memphis: IEEE, 2002: 217-220.
13.	De Chazal P, Heneghan C. Automated assessment of atrial fibrillation// Computers in Cardiology 2001. Rotterdam: IEEE, 2001: 117-120.
14.	Boon K H, Khalil-Hani M, Malarvili M B, et al. Paroxysmal atrial fibrillation prediction method with shorter HRV sequences. Comput Meth Prog Bio, 2016, 134: 187-196.
15.	Boon K H, Khalil-Hani M, Malarvili M B. Paroxysmal atrial fibrillation prediction based on HRV analysis and non-dominated sorting genetic algorithm III. Comput Meth Prog Bio, 2018, 153: 171-184.
16.	Mohebbi M, Ghassemian H. Prediction of paroxysmal atrial fibrillation based on non-linear analysis and spectrum and bispectrum features of the heart rate variability signal. Comput Meth Prog Bio, 2012, 105(1): 40-49.
17.	Narin A, Isler Y, Ozer M, et al. Early prediction of paroxysmal atrial fibrillation based on short-term heart rate variability. Physica A, 2018, 509: 56-65.
18.	兰天杰, 杨翠微. 基于RR间期的阵发性房颤复发预测. 生物医学工程学杂志, 2019, 36(4): 521-530.
19.	Sutton J R, Mahajan R, Akbilgic O, et al. PhysOnline: an open source machine learning pipeline for real-time analysis of streaming physiological waveform. IEEE J Biomed Health, 2018, 23(1): 59-65.
20.	Mendez M M, Hsu M C, Yuan J T, et al. A heart rate variability-based paroxysmal atrial fibrillation prediction system. Appl Sci-Basel, 2022, 12(5): 2387.
21.	Erdenebayar U, Kim H, Park J U, et al. Automatic prediction of atrial fibrillation based on convolutional neural network using a short-term normal electrocardiogram signal. J Korean Med Sci, 2019, 34(7): e64.
22.	Attia Z I, Noseworthy P A, Lopez-Jimenez F, et al. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet, 2019, 394(10201): 861-867.
23.	杨萍, 王丹, 康子健, 等. 基于模式识别和集成CNN-LSTM的阵发性房颤预测模型. 浙江大学学报(工学版), 2020, 54(5): 1039-1048.
24.	Li M, He G, Zhu B. Atrial fibrillation detection based on the combination of depth and statistical features of ECG// Proceedings of the 3rd International Conference on Graphics and Signal Processing. Hong Kong: ACM, 2019: 105-112.
25.	Ma C, Wei S, Chen T, et al. Integration of results from convolutional neural network in a support vector machine for the detection of atrial fibrillation. IEEE T Instrum Meas, 2020, 70: 1-10.
26.	Li H, Wang X, Liu C, et al. Dual-input neural network integrating feature extraction and deep learning for coronary artery disease detection using electrocardiogram and phonocardiogram. IEEE Access, 2019, 7: 146457-146469.
27.	Ghosh S K, Ponnalagu R N. Investigation of discrete wavelet transform domain optimal parametric approach for denoising of phonocardiogram signal. J Mech Med Biol, 2022, 22(6): 2250046.
28.	Ahmad Z, Tabassum A, Guan L, et al. ECG heartbeat classification using multimodal fusion. IEEE Access, 2021, 9: 100615-100626.
29.	Peng H, Long F, Ding C. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE T Pattern Anal, 2005, 27(8): 1226-1238.
30.	Liu H, Setiono R. Chi2: Feature selection and discretization of numeric attributes// Proceedings of 7th IEEE International Conference on Tools with Artificial Intelligence. Herndon: IEEE, 1995: 388-391.
31.	Spolaôr N, Cherman E A, Monard M C, et al. ReliefF for multi-label feature selection// 2013 Brazilian Conference on Intelligent Systems. Fortaleza: IEEE, 2013: 6-11.
32.	Ali Khan S, Hussain A, Basit A, et al. Kruskal-Wallis-based computationally efficient feature selection for face recognition. Sci World J, 2014, 2014: 672630.

1. Camm A J, Naccarelli G V, Mittal S, et al. The increasing role of rhythm control in patients with atrial fibrillation: JACC state-of-the-art review. J Am Coll Cardiol, 2022, 79(19): 1932-1948.
2. Guo Y, Wang H, Zhang H, et al. Mobile photoplethysmographic technology to detect atrial fibrillation. J Am Coll Cardiol, 2019, 74(19): 2365-2375.
3. Kuniss M, Pavlovic N, Velagic V, et al. Cryoballoon ablation vs. antiarrhythmic drugs: first-line therapy for patients with paroxysmal atrial fibrillation. EP Europace, 2021, 23(7): 1033-1041.
4. Al-Khatib S M, Wilkinson W E, Sanders L L, et al. Observations on the transition from intermittent to permanent atrial fibrillation. Am Heart J, 2000, 140(1): 142-145.
5. Schnabel R B, Yin X, Gona P, et al. 50 year trends in atrial fibrillation prevalence, incidence, risk factors, and mortality in the Framingham Heart Study: a cohort study. Lancet, 2015, 386(9989): 154-162.
6. German D M, Kabir M M, Dewland T A, et al. Atrial fibrillation predictors: importance of the electrocardiogram. Ann Noninvas Electro, 2016, 21(1): 20-29.
7. Pipilas D, Friedman S F, Khurshid S. The use of artificial intelligence to predict the development of atrial fibrillation. Curr Cardiol Rep, 2023, 25(5): 381-389.
8. Moody G B, Goldberger A L, Mcclennen S, et al. Predicting the onset of paroxysmal atrial fibrillation: the Computers in Cardiology Challenge 2001// Computers in Cardiology 2001. Rotterdam: IEEE, 2001: 113-116.
9. Zong W, Mukkamala R, Mark R G. A methodology for predicting paroxysmal atrial fibrillation based on ECG arrhythmia feature analysis// Computers in Cardiology 2001. Rotterdam: IEEE, 2001: 125-128.
10. Thong T, McNames J, Aboy M, et al. Prediction of paroxysmal atrial fibrillation by analysis of atrial premature complexes. IEEE T Bio-Med Eng, 2004, 51(4): 561-569.
11. Thong T, McNames J, Aboy M, et al. Paroxysmal atrial fibrillation prediction using isolated premature atrial events and paroxysmal atrial tachycardia// Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Cancun: IEEE, 2003: 163-166.
12. Hickey B, Heneghan C. Screening for paroxysmal atrial fibrillation using atrial premature contractions and spectral measures// Computers in Cardiology 2002. Memphis: IEEE, 2002: 217-220.
13. De Chazal P, Heneghan C. Automated assessment of atrial fibrillation// Computers in Cardiology 2001. Rotterdam: IEEE, 2001: 117-120.
14. Boon K H, Khalil-Hani M, Malarvili M B, et al. Paroxysmal atrial fibrillation prediction method with shorter HRV sequences. Comput Meth Prog Bio, 2016, 134: 187-196.
15. Boon K H, Khalil-Hani M, Malarvili M B. Paroxysmal atrial fibrillation prediction based on HRV analysis and non-dominated sorting genetic algorithm III. Comput Meth Prog Bio, 2018, 153: 171-184.
16. Mohebbi M, Ghassemian H. Prediction of paroxysmal atrial fibrillation based on non-linear analysis and spectrum and bispectrum features of the heart rate variability signal. Comput Meth Prog Bio, 2012, 105(1): 40-49.
17. Narin A, Isler Y, Ozer M, et al. Early prediction of paroxysmal atrial fibrillation based on short-term heart rate variability. Physica A, 2018, 509: 56-65.
18. 兰天杰, 杨翠微. 基于RR间期的阵发性房颤复发预测. 生物医学工程学杂志, 2019, 36(4): 521-530.
19. Sutton J R, Mahajan R, Akbilgic O, et al. PhysOnline: an open source machine learning pipeline for real-time analysis of streaming physiological waveform. IEEE J Biomed Health, 2018, 23(1): 59-65.
20. Mendez M M, Hsu M C, Yuan J T, et al. A heart rate variability-based paroxysmal atrial fibrillation prediction system. Appl Sci-Basel, 2022, 12(5): 2387.
21. Erdenebayar U, Kim H, Park J U, et al. Automatic prediction of atrial fibrillation based on convolutional neural network using a short-term normal electrocardiogram signal. J Korean Med Sci, 2019, 34(7): e64.
22. Attia Z I, Noseworthy P A, Lopez-Jimenez F, et al. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet, 2019, 394(10201): 861-867.
23. 杨萍, 王丹, 康子健, 等. 基于模式识别和集成CNN-LSTM的阵发性房颤预测模型. 浙江大学学报(工学版), 2020, 54(5): 1039-1048.
24. Li M, He G, Zhu B. Atrial fibrillation detection based on the combination of depth and statistical features of ECG// Proceedings of the 3rd International Conference on Graphics and Signal Processing. Hong Kong: ACM, 2019: 105-112.
25. Ma C, Wei S, Chen T, et al. Integration of results from convolutional neural network in a support vector machine for the detection of atrial fibrillation. IEEE T Instrum Meas, 2020, 70: 1-10.
26. Li H, Wang X, Liu C, et al. Dual-input neural network integrating feature extraction and deep learning for coronary artery disease detection using electrocardiogram and phonocardiogram. IEEE Access, 2019, 7: 146457-146469.
27. Ghosh S K, Ponnalagu R N. Investigation of discrete wavelet transform domain optimal parametric approach for denoising of phonocardiogram signal. J Mech Med Biol, 2022, 22(6): 2250046.
28. Ahmad Z, Tabassum A, Guan L, et al. ECG heartbeat classification using multimodal fusion. IEEE Access, 2021, 9: 100615-100626.
29. Peng H, Long F, Ding C. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE T Pattern Anal, 2005, 27(8): 1226-1238.
30. Liu H, Setiono R. Chi2: Feature selection and discretization of numeric attributes// Proceedings of 7th IEEE International Conference on Tools with Artificial Intelligence. Herndon: IEEE, 1995: 388-391.
31. Spolaôr N, Cherman E A, Monard M C, et al. ReliefF for multi-label feature selection// 2013 Brazilian Conference on Intelligent Systems. Fortaleza: IEEE, 2013: 6-11.
32. Ali Khan S, Hussain A, Basit A, et al. Kruskal-Wallis-based computationally efficient feature selection for face recognition. Sci World J, 2014, 2014: 672630.

Journal of Biomedical Engineering

Prediction method of paroxysmal atrial fibrillation based on multimodal feature fusion

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