An Algorithm for Robust and Efficient Location of T-Wave Ends in Electrocardiograms

The purpose of this paper is to propose a new algorithm for T-wave end location in electrocardiograms, mainly through the computation of an indicator related to the area covered by the T-wave curve. Based on simple assumptions, essentially on the concavity of the T-wave form, it is formally proved that the maximum of the computed indicator inside each cardiac cycle coincides with the T-wave end. Moreover, the algorithm is robust to acquisition noise, to wave form morphological variations and to baseline wander. It is also computationally very simple: the main computation can be implemented as a simple finite impulse response filter. When evaluated with the PhysioNet QT database in terms of the mean and the standard deviation of the T-wave end location errors, the proposed algorithm outperforms the other algorithms evaluated with the same database, according to the most recent available publications up to our knowledge

[1]  Senén Barro,et al.  A new approach for TU complex characterization , 2000, IEEE Transactions on Biomedical Engineering.

[2]  S Abboud,et al.  The use of cross-correlation function for the alignment of ECG waveforms and rejection of extrasystoles. , 1984, Computers and biomedical research, an international journal.

[3]  K G Lindecrantz,et al.  New software QRS detector algorithm suitable for real time applications with low signal-to-noise ratios. , 1988, Journal of biomedical engineering.

[4]  M. Sorine,et al.  Short term control of the cardiovascular system: Assessment with the isometric handgrip exercise , 2004 .

[5]  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.

[6]  Thorsten Last,et al.  Multi-component based cross correlation beat detection in electrocardiogram analysis , 2004, Biomedical engineering online.

[7]  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.

[8]  P. Schwartz,et al.  QT interval prolongation as predictor of sudden death in patients with myocardial infarction. , 1978, Circulation.

[9]  M Malik,et al.  Computer model of cardiac repolarization processes and of the recovery sequence. , 1989, Computers and biomedical research, an international journal.

[10]  Pawel Strumillo,et al.  Nested median filtering for detecting T-wave offset in ECGs , 2002 .

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

[12]  C. Li,et al.  Detection of ECG characteristic points using wavelet transforms. , 1995, IEEE transactions on bio-medical engineering.

[13]  A J Camm,et al.  Algebraic Decomposition of the TU Wave Morphology Patterns , 1995, Pacing and clinical electrophysiology : PACE.

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

[15]  A. Moss,et al.  The long QT syndrome: a prospective international study. , 1985, Circulation.

[16]  C. Medigue,et al.  Robust and efficient location of T-wave ends in electrocardiogram , 2005, Computers in Cardiology, 2005.

[17]  Claire Médigue,et al.  Heart rate variability during exercise performed below and above ventilatory threshold. , 2004, Medicine and science in sports and exercise.

[18]  R. Orglmeister,et al.  The principles of software QRS detection , 2002, IEEE Engineering in Medicine and Biology Magazine.