Comparison of Atrial Fibrillation Detection Performance Using Decision Trees, SVM and Artificial Neural Network

Atrial fibrillation (AFib) is a supraventricular tachyarrhythmia characterized by uncoordinated atrial activation and ineffective atrial contraction. AFib affects 1–2% of the general population, its prevalence increases with age and may remain long undiagnosed. Due to costs of hospitalization and treatment related to AFib and increasing prevalence, effective methods of detecting atrial fibrillation are needed.

[1]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[2]  Bernhard Schölkopf,et al.  Extracting Support Data for a Given Task , 1995, KDD.

[3]  S Dash,et al.  A statistical approach for accurate detection of atrial fibrillation and flutter , 2009, 2009 36th Annual Computers in Cardiology Conference (CinC).

[4]  G.B. Moody,et al.  The impact of the MIT-BIH Arrhythmia Database , 2001, IEEE Engineering in Medicine and Biology Magazine.

[5]  Chao Huang,et al.  A Novel Method for Detection of the Transition Between Atrial Fibrillation and Sinus Rhythm , 2011, IEEE Transactions on Biomedical Engineering.

[6]  S. Hulley,et al.  Designing clinical research , 2013 .

[7]  Ewaryst J. Tkacz,et al.  Feature extraction based on time-frequency and Independent Component Analysis for improvement of separation ability in Atrial Fibrillation detector , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[8]  P. Kirchhof,et al.  2016 ESC Guidelines for the management of atrial fibrillation developed in collaboration with EACTS. , 2016, European heart journal.

[9]  Willis J. Tompkins,et al.  A Real-Time QRS Detection Algorithm , 1985, IEEE Transactions on Biomedical Engineering.

[10]  Marimuthu Palaniswami,et al.  Analyzing temporal variability of standard descriptors of Poincaré plots. , 2010, Journal of electrocardiology.

[11]  G. Breithardt,et al.  Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. , 1996 .

[12]  H. Ghassemian,et al.  Detection of atrial fibrillation episodes using SVM , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[13]  M. Ezekowitz,et al.  2014 AHA/ACC/HRS guideline 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 Heart Rhythm Society. , 2014, Circulation.

[14]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[15]  Leon Glass,et al.  A method for detection of atrial fibrillation using RR intervals , 2000, Computers in Cardiology 2000. Vol.27 (Cat. 00CH37163).

[16]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[17]  Federico Girosi,et al.  Training support vector machines: an application to face detection , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[18]  J. Wiesel,et al.  Detection of atrial fibrillation using a modified microlife blood pressure monitor. , 2009, American journal of hypertension.

[19]  Changchun Liu,et al.  Automatic detection of atrial fibrillation using R-R interval signal , 2011, 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI).

[20]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[21]  Pawel Kostka,et al.  Feature extraction in time-frequency signal analysis by means of matched wavelets as a feature generator , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[22]  T. Seppänen,et al.  Quantitative beat-to-beat analysis of heart rate dynamics during exercise. , 1996, The American journal of physiology.

[23]  Vias Markides,et al.  Atrial fibrillation: classification, pathophysiology, mechanisms and drug treatment , 2003, Heart.

[24]  Marimuthu Palaniswami,et al.  Do existing measures of Poincare plot geometry reflect nonlinear features of heart rate variability? , 2001, IEEE Transactions on Biomedical Engineering.

[25]  I. Romero,et al.  Comparative study of algorithms for Atrial Fibrillation detection , 2011, 2011 Computing in Cardiology.

[26]  P. Kirchhof,et al.  2016 ESC Guidelines for the management of atrial fibrillation developed in collaboration with EACTS. , 2016, Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology.