Abstract
A prediction algorithm for paroxysmal atrial fibrillation (PAF) is proposed. The algorithm is based on heart rate variability (HRV) analysis of Electrocardiogram (ECG) signal. Genetic Algorithm (GA) is applied to simultaneously optimize the HRV feature subset and parameter tuning of the classifier in the algorithm. Experimental result shows, with single hold-out validation, the proposed algorithm achieve prediction accuracy of 92.9%, with 96.4% sensitivity and 89.4% specificity. With a 10-fold cross validation, which give truer indication of our classifier generalization capability, the proposed algorithm obtain 86.8% of accuracy and the feature subset count is minimized to only 13. These results almost outperform most previous works.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Camm, A.J., et al.: Guidelines for the management of atrial fibrillation The Task Force for the Management of Atrial Fibrillation of the European Society of Cardiology (ESC). Eur. Heart J. 31 (2010)
Prakash, A., Saksena, S., Hill, M., Krol, R.B., Munsif, A.N., Giorgberidze, I., Mathew, P., Mehra, R.: Acute effects of dual site right atrial pacing in patients with spontaneous and inducible atrial flutter and fibrillation. J. Am. Coll. Cardiol. 29(5), 1007–1014 (1997)
Prystowsky, N.: Management of atrial fibrillation: therapeutic options and clinical decisions. Am. J. Cardiol. 85(10A), 3D–11D.E (2000)
Thong, T., McNames, J., Aboy, M., Goldstein, B.: Prediction of paroxysmal atrial fibrillation by analysis of atrial premature complexes. IEEE Trans. Biomed. Eng. 51(4), 561–569 (2004)
Lynn, K.S., Chiang, H.D.: A two-stage solution algorithm for paroxysmal atrial fibrillation. In: Proceedings of the Computers in Cardiology, pp. 405–407 (2001)
Yang, A.C.C., Yin, H.W.: Prediction of paroxysmal atrial fibrillation by foot print analysis. In: Proceedings of the Computers in Cardiology, pp. 401–404 (2001)
Chesnokov, Y.V.: Complexity and spectral analysis of the heart rate variability dynamics for distant prediction of paroxysmal atrial fibrillation with artificial intelligence methods. Artif. Intell. Med. 43(2), 151–165 (2008)
Mohebbi, M., Ghassemian, H.: Prediction of paroxysmal atrial fibrillation based on non-linear analysis and spectrum and bispectrum features of heart rate variability signal. Comput. Methods Programs Biomed. 105 (2012) Elsevier
Costin, H., Rotariu, C., Pasarica, A.: Atrial fibrillation onset prediction using variability of ECG signals. In: 2013 8th International Symposium on Advanced Topics in Electrical Engineering (ATEE), pp. 1, 4, 23–25 (2013)
Chang, C.-C. Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2:27:1–27:27 (2011). Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
Bengio, Yoshua, Grandvalet, Yves: No unbiased estimator of the variance of K-fold cross-validation. J. Mach. Learn. Res. 5, 1089–1105 (2004)
İşler, Y., Kuntalp, M.: Combining classical HRV indices with wavelet entropy measures improves to performance in diagnosing congestive heart failure. Comput. Biol. Med. 37(10), 1502–1510 (2007). ISSN 0010-4825
Yu, S.-N., Lee, M.-Y.: Bispectral analysis and genetic algorithm for congestive heart failure recognition based on heart rate variability. Comput. Biol. Med. 42(8), 816–825 (2012). ISSN 0010-4825
Saeys, Y., Inza, I., Larranaga, P.: A review of feature selection techniques in bioinformatics. Oxford J. Bioinform. 23(19), 2507–2517 (2007)
Huang, C.-L. Wang, C.-J.: A GA-based feature selection and parameters optimization for support vector machines. Expert Syst. Appl. 31(2), 231–240 (2006). ISSN 0957-4174
Physionet AFPDB Database, http://www.physionet.org/physiobank/database/afpdb
Pan, J., Tompkins, W.J.: A real time QRS detection algorithm. IEEE Trans. Biomed. Eng. 32(3), 230–236 (1985)
Rajendra Acharya, U., et al.: Heart rate variability: a review. Med. Biol. Eng. Comput. 44(12), 1031–1051 (2006)
Nikias, C.L., Raghuveer, M.R.: Bispectrum estimation: a digital signal processing framework. Proc. IEEE 75(7), 869, 891 (1987)
Zhou, S.-M., Gan, J.Q., Sepulveda, F.: Classifying mental tasks based on features of higher-order statistics from EEG signals in brain-computer interface. Inf. Sci. 178(6), 1629–1640 (2008)
Acknowledgements
This work is supported by Universiti Teknologi Malaysia (UTM) and the Ministry of Science, Tech. & Innovation of Malaysia (MOSTI) under the TechnoFund Grant No. 3H001.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Boon, K.H., Khalil-Hani, M., Sia, C.W. (2019). Paroxysmal Atrial Fibrillation Onset Prediction Using Heart Rate Variability Analysis and Genetic Algorithm for Optimization. In: Abawajy, J., Othman, M., Ghazali, R., Deris, M., Mahdin, H., Herawan, T. (eds) Proceedings of the International Conference on Data Engineering 2015 (DaEng-2015) . Lecture Notes in Electrical Engineering, vol 520. Springer, Singapore. https://doi.org/10.1007/978-981-13-1799-6_62
Download citation
DOI: https://doi.org/10.1007/978-981-13-1799-6_62
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1797-2
Online ISBN: 978-981-13-1799-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)