P wave detection in ECG signals using an extended Kalman filter: an evaluation in different arrhythmia contexts

Monitoring atrial activity via P waves, is an important feature of the arrhythmia detection procedure. The aim of this paper is to present an algorithm for P wave detection in normal and some abnormal records by improving existing methods in the field of signal processing. In contrast to the classical approaches, which are completely blind to signal dynamics, our proposed method uses the extended Kalman filter, EKF25, to estimate the state variables of the equations modeling the dynamic of an ECG signal. This method is a modified version of the nonlinear dynamical model previously introduced for a generation of synthetic ECG signals and fiducial point extraction in normal ones. It is capable of estimating the separate types of activity of the heart with reasonable accuracy and performs well in the presence of morphological variations in the waveforms and ectopic beats. The MIT-BIH Arrhythmia and QT databases have been used to evaluate the performance of the proposed method. The results show that this method has Se  =  98.38% and Pr  =  96.74% in the overall records (considering normal and abnormal rhythms).

[1]  Christian Jutten,et al.  A Nonlinear Bayesian Filtering Framework for ECG Denoising , 2007, IEEE Transactions on Biomedical Engineering.

[2]  Karsten Sternickel,et al.  Automatic pattern recognition in ECG time series , 2002, Comput. Methods Programs Biomed..

[3]  Bachir Boucheham,et al.  Digital fractional order differentiation-based algorithm for P and T-waves detection and delineation , 2005 .

[4]  François Portet,et al.  P wave detector with PP rhythm tracking: evaluation in different arrhythmia contexts , 2008, Physiological measurement.

[5]  Guy Amit,et al.  Atrial Electrical Activity Detection Using Linear Combination of 12-Lead ECG Signals , 2014, IEEE Transactions on Biomedical Engineering.

[6]  P Caminal,et al.  Automatic detection of wave boundaries in multilead ECG signals: validation with the CSE database. , 1994, Computers and biomedical research, an international journal.

[7]  Marie-Odile Cordier,et al.  Temporal abstraction and inductive logic programming for arrhythmia recognition from electrocardiograms , 2003, Artif. Intell. Medicine.

[8]  Guy Carrault,et al.  Atrial activity enhancement by Wiener filtering using an artificial neural network , 2001, IEEE Transactions on Biomedical Engineering.

[9]  Patrick E. McSharry,et al.  A dynamical model for generating synthetic electrocardiogram signals , 2003, IEEE Transactions on Biomedical Engineering.

[10]  Reza Sameni,et al.  Extraction of Fetal Cardiac Signals from an Array of Maternal Abdominal Recordings , 2008 .

[11]  Ali H. Shoeb,et al.  Model-based filtering, compression and classification of the ECG , 2005 .

[12]  Pablo Laguna,et al.  A wavelet-based ECG delineator: evaluation on standard databases , 2004, IEEE Transactions on Biomedical Engineering.

[13]  Mónica F. Bugallo,et al.  ECG denoising using a dynamical model and a marginalized particle filter , 2011, 2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR).

[14]  Shamim Nemati,et al.  Semisupervised ECG Ventricular Beat Classification With Novelty Detection Based on Switching Kalman Filters , 2015, IEEE Transactions on Biomedical Engineering.

[15]  Gari D Clifford,et al.  Synthetic ECG generation and Bayesian filtering using a Gaussian wave-based dynamical model , 2010, Physiological measurement.

[16]  Pablo Laguna,et al.  A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG , 1997, Computers in Cardiology 1997.

[17]  I. Dotsinsky.  Atrial wave detection algorithm for discovery of some rhythm abnormalities , 2007, Physiological measurement.

[18]  Jean-Marc Boucher,et al.  Hidden Markov tree model applied to ECG delineation , 2005, IEEE Transactions on Instrumentation and Measurement.

[19]  Christian Jutten,et al.  ECG denoising using angular velocity as a state and an observation in an Extended Kalman Filter framework , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[20]  M B Shamsollahi,et al.  A model-based Bayesian framework for ECG beat segmentation , 2009, Physiological measurement.

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