Interpretation of normal and pathological beats using multiresolution wavelet analysis

The discrete wavelet transform has great capability to analyze the temporal and spectral properties of non stationary signal like electrocardiogram (ECG). In this paper, we developed and evaluated a robust algorithm using multiresolution analysis based on the discrete wavelet transform (DWT) for twelve-lead ECG temporal feature extraction. The study, with support of physiological knowledge, attempted interpretation of ECG beats with different patterns. Selection of appropriate group of wavelet coefficients along with decision rules is used to determine P, Q, R, S and T wave locations, amplitudes, onsets and offsets We evaluated the algorithm on normal and abnormal beats from various manually annotated databases from physiobank with different sampling frequency. An appropriate value of threshold at QRS detector offered sensitivity of 99.5% and positive predictivity of 98.9% over the first lead of the MIT-BIH Arrhythmia Database.

[1]  S. Cerutti,et al.  ECG fiducial points detection through wavelet transform , 1995, Proceedings of 17th International Conference of the Engineering in Medicine and Biology Society.

[2]  Liqing Zhang,et al.  ECG Feature Extraction and Classification Using Wavelet Transform and Support Vector Machines , 2005, 2005 International Conference on Neural Networks and Brain.

[3]  Wen-June Wang,et al.  QRS complexes detection for ECG signal: The Difference Operation Method , 2008, Comput. Methods Programs Biomed..

[4]  Suhas Gajre,et al.  A Hybrid Algorithm for Classification of Compressed ECG , 2012 .

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

[6]  Szi-Wen Chen,et al.  A real-time QRS detection method based on moving-averaging incorporating with wavelet denoising , 2006, Comput. Methods Programs Biomed..

[7]  M. Mitra,et al.  Detection of ECG characteristic points using Multiresolution Wavelet Analysis based Selective Coefficient Method , 2010 .

[8]  G. Boudreaux-Bartels,et al.  Wavelet transform-based QRS complex detector , 1999, IEEE Transactions on Biomedical Engineering.

[10]  Measurement , 2007 .

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

[12]  S. S. Mehta,et al.  Detection of QRS Complexes in 12-lead ECG using Adaptive Quantized Threshold , 2008 .

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

[14]  V. Chouhan,et al.  Threshold-based Detection of P and T-wave in ECG using New Feature Signal , 2008 .

[15]  S. T. Hamde,et al.  Feature extraction from ECG signals using wavelet transforms for disease diagnostics , 2002, Int. J. Syst. Sci..

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