Abstract
Atrial Fibrillation (AF) is a common cardiac arrhythmia, and it has a high rate of morbidity and mortality. In this paper, an algorithm for automatic AF episodes detection based on novel low computational cost features is proposed. The features are based on Poincare plots calculated from heart rate variability signal. A supervised classification technique, Support Vector Machines, optimized with Particle Swarm Optimization, was implemented. The data was obtained from MIT-BIH Atrial Fibrillation and Normal Sinus Rhythm Databases. This method shows an accuracy of 92.9% to detect AF spontaneous episodes in signals from AF patients, and 97.8% to classify between AF episodes from AF patients and episodes from subjects with normal sinus rhythm. The proposed method can be employed in real time applications due to its performance as well for its low computation time around 8.8 ms per episode.
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References
Chen J, Yang Z G, Xu H Y, Shi K, Long Q H, Guo Y K. Assessments of pulmonary vein and left atrial anatomical variants in atrial fibrillation patients for catheter ablation with cardiac CT Eur Radiol. 2016.
Mohebbi Maryam, Ghassemian Hassan. Prediction of paroxysmal atrial fibrillation based on non-linear analysis and spectrum and bispectrum features of the heart rate variability signal Computer Methods and Programs in Biomedicine. 2012;105:40–49.
Fuster Valentin, Rydén Lars E, Cannom David S, et al. ACC/AHA/ESC 2006 Guidelines for the Management of Patients with Atrial Fibrillation: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and the European Society of Cardiology Committee for Practice Circulation. 2006;114:257–354.
Yoon Kwon Ha, Thap Tharoeun, Jeong Chang Won, et al. Analysis of Statistical Methods for Automatic Detection of Congestive Heart Failure and Atrial Fibrillation with Short RR Interval Time Series in 2015 9th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing: 452–457 2015.
Ghodrati Alireza, Murray Bill, Marinello Stephen. RR Interval Analysis for Detection of Atrial Fibrillation in ECG Monitors in Annual International Conference of the IEEE Engineering in Medicine and Biology Society;2008:601–604 2008.
Lian Jie, Wang Lian, Muessig Dirk. A simple method to detect atrial fibrillation using RR intervals. American journal of cardiology. 2011;107:1494–1497.
Asgari Shadnaz, Mehrnia Alireza, Moussavi Maryam. Automatic detection of atrial fibrillation using stationary wavelet transform and support vector machine Computers in Biology and Medicine. 2015;60:132–142.
Petrenas Andrius, Marozas Vaidotas, Lukosevicius Arünas, Sörnmo Leif. A Noise-Adaptive Method for Detection of Brief Episodes of Paroxysmal Atrial Fibrillation in Computers in Cardiology:739–742 2013.
Yaghouby Farid, Ayatollahi Ahmad, Bahramali Reihaneh, Yaghouby Maryam, Alavi Amir Hossein. Towards automatic detection of atrial fibrillation: A hybrid computational approach Computers in Biology and Medicine. 2010;40:919–930.
Tateno K., Glass L.. A method for detection of atrial fibrillation using RR intervals in Computers in Cardiology 2000. Vol.27 (Cat. 00CH37163):391–394 2000.
Ruan Xiuhua, Liu Changchun, Liu Chengyu, Wang Xinpei, Li Peng. Automatic detection of atrial fibrillation using R-R interval signal in The 4th International Conference on Biomedical Engineering and Informatics (BMEI 2011):644–7 2011.
Goldberger A. L., Amaral L.A.N., Glass L., et al. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals Circulation. 2000;101:215–220.
Pan J, Tompkins W J. A real-time QRS detection algorithm. IEEE transactions on bio-medical engineering. 1985;32:230–236.
Brennan M., Palaniswami M., Kamen P.. Do existing measures of Poincar?? plot geometry reflect nonlinear features of heart rate variability? IEEE Transactions on Biomedical Engineering. 2001;48:1342–1347.
Peña Daniel, Rodríguez Julio. Descriptive measures of multivariate scatter and linear dependence Journal of Multivariate Analysis. 2003;85:361–374.
Maji U., Mitra M., Pal S.. Differentiating normal sinus rhythm and atrial fibrillation in ECG signal: A phase rectified signal averaging based approach in International Conference on Control, Instrumentation, Energy and Communication, CIEC 2014:176–180 2014.
Burges Christopher J.C.. A Tutorial on Support Vector Machines for Pattern Recognition Data Mining and Knowledge Discovery. 1998;2:121–167.
Kennedy J, Eberhart R. Particle swarm optimization Neural Networks, 1995. Proceedings., IEEE International Conference on. 1995;4:1942–1948.
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Sepulveda-Suescun, J.P., Murillo-Escobar, J., Urda-Benitez, R.D., Orrego-Metaute, D.A., Orozco-Duque, A. (2017). Atrial fibrillation detection through heart rate variability using a machine learning approach and Poincare plot features. In: Torres, I., Bustamante, J., Sierra, D. (eds) VII Latin American Congress on Biomedical Engineering CLAIB 2016, Bucaramanga, Santander, Colombia, October 26th -28th, 2016. IFMBE Proceedings, vol 60. Springer, Singapore. https://doi.org/10.1007/978-981-10-4086-3_142
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DOI: https://doi.org/10.1007/978-981-10-4086-3_142
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