Characterizing Atrial Fibrillation in Empirical Mode Decomposition Domain

AbstractAutomatic patient monitoring for cardiac diseases requires the detection of various abnormal electrical activities occurring in the heart. Pseudo-pacemaker activities by cardiac cells or non-standard electrical impulses in the atria or ventricle cause various types of arrhythmia. Atrial fibrillation (AF) is the most important irregularity that occurs due to electrical malfunctioning in the atria. Unsynchronized pumping of the atria and ventricle causes different volumes of blood flow at different rhythms. In this paper, a method for the automatic detection of AF in the empirical mode decomposition (EMD) domain is proposed. Being a completely data-adaptive technique, EMD can be applied to all kinds of electrocardiogram signals. Selective reconstruction eliminates the requirement of conventional preprocessing for denoising the raw data. A combination of temporal and statistical features is used to characterize abnormal rhythms. A novel SQ time deviation feature is introduced, which is proved to be a good choice for AF classification. A nonlinear-kernel-based support vector machine classifier is used for classification. Sensitivity, specificity, and accuracy comparable to those of previous works are achieved for the MIT-BIH Arrhythmia Database.

[1]  Madhuchhanda Mitra,et al.  Empirical mode decomposition based ECG enhancement and QRS detection , 2012, Comput. Biol. Medicine.

[2]  S. Graja,et al.  SVM Classification of patients prone to atrial fibrillation , 2005, IEEE International Workshop on Intelligent Signal Processing, 2005..

[3]  Giuseppe Boriani,et al.  The epidemiologic threat of atrial fibrillation: need for secondary, primary, and primordial prevention. , 2015, Chest.

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

[5]  Hualou Liang,et al.  Application of the empirical mode decomposition to the analysis of esophageal manometric data in gastroesophageal reflux disease , 2005, IEEE Transactions on Biomedical Engineering.

[6]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[7]  Sophia Zhou,et al.  An automated algorithm for the detection of atrial fibrillation in the presence of paced rhythms , 2010, 2010 Computing in Cardiology.

[8]  Mohammed Imamul Hassan Bhuiyan,et al.  Detection of Seizure and Epilepsy Using Higher Order Statistics in the EMD Domain , 2013, IEEE Journal of Biomedical and Health Informatics.

[9]  Y C Fung,et al.  Engineering analysis of biological variables: an example of blood pressure over 1 day. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[10]  D. Levy,et al.  Impact of atrial fibrillation on the risk of death: the Framingham Heart Study. , 1998, Circulation.

[11]  Raúl Alcaraz,et al.  Classification of Paroxysmal and Persistent Atrial Fibrillation in Ambulatory ECG Recordings , 2011, IEEE Transactions on Biomedical Engineering.

[12]  Ki H. Chon,et al.  Atrial Fibrillation Detection Using an iPhone 4S , 2013, IEEE Transactions on Biomedical Engineering.

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

[14]  Karen L. Furie,et al.  Stroke Associated with Atrial Fibrillation – Incidence and Early Outcomes in the North Dublin Population Stroke Study , 2009, Cerebrovascular Diseases.

[15]  Vicente Zarzoso,et al.  Spatial Variability of the 12-Lead Surface ECG as a Tool for Noninvasive Prediction of Catheter Ablation Outcome in Persistent Atrial Fibrillation , 2013, IEEE Transactions on Biomedical Engineering.

[16]  Steffen Leonhardt,et al.  Automatic Detection of Atrial Fibrillation in Cardiac Vibration Signals , 2013, IEEE Journal of Biomedical and Health Informatics.

[17]  Richard P. M. Houben,et al.  Analysis of Fractionated Atrial Fibrillation Electrograms by Wavelet Decomposition , 2010, IEEE Transactions on Biomedical Engineering.

[18]  Uday Maji,et al.  Automatic Detection of Atrial Fibrillation Using Empirical Mode Decomposition and Statistical Approach , 2013 .

[19]  J.-M. Boucher,et al.  ECG segmentation and P-wave feature extraction: application to patients prone to atrial fibrillation , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[20]  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).

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

[22]  David Atienza,et al.  Automated real-time atrial fibrillation detection on a wearable wireless sensor platform , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[23]  Ki H. Chon,et al.  Automatic Motion and Noise Artifact Detection in Holter ECG Data Using Empirical Mode Decomposition and Statistical Approaches , 2012, IEEE Transactions on Biomedical Engineering.

[24]  M Mitra,et al.  Study of atrial activities for abnormality detection by phase rectified signal averaging technique , 2015, Journal of medical engineering & technology.

[25]  Ki H. Chon,et al.  Time-Varying Coherence Function for Atrial Fibrillation Detection , 2013, IEEE Transactions on Biomedical Engineering.

[26]  James McNames,et al.  Prediction of paroxysmal atrial fibrillation by analysis of atrial premature complexes , 2004, IEEE Transactions on Biomedical Engineering.