Noised abnormal ECG signal analysis by combining EMD and Choi-Williams techniques

Due to the wide variety of biomedical signals, such as the electrocardiogram (ECG) signal, and problems encountered in medicine and biology a good diagnostic is very important to detect the real pathology contained in the signal. In one hand the presence of the noise and in the other hand the non-stationary multicomponent nature of the electrocardiogram (ECG) signal present the major problems that can influence in the analysis of such biomedical signal. In this paper, the Empirical Mode Decomposition (EMD) and the Choi-Williams time frequency technique were applied to a noised abnormal ECG signal. In the first time, the EMD method is used as a pretreatment to filter the noise from the noised abnormal ECG signal. In the second time, the Choi-Williams time-frequency technique is applied to extract different components of the resulting signal, especially the QRS complexes, and detecting the anomaly present in the latter. The abnormal studied signal was taken from a patient with supraventricular arrhythmia. The analysis results show that the combination of the EMD method and the Choi-Williams time-frequency technique can be an effective approach to analyze the noised abnormal ECG signals.

[1]  W. Hueston,et al.  Management of common arrhythmias: Part II. Ventricular arrhythmias and arrhythmias in special populations. , 2002, American family physician.

[2]  Patrick E. McSharry,et al.  Advanced Methods And Tools for ECG Data Analysis , 2006 .

[3]  G. Maze,et al.  Détermination de l'Épaisseur d'un Tube élastique à partir de l'Analyse Temps-Fréquence de Wigner-Ville , 2009 .

[4]  D. T. Barry,et al.  Time-frequency analysis of skeletal muscle and cardiac vibrations , 1996, Proc. IEEE.

[5]  Elif Derya Übeyli,et al.  ECG beat classifier designed by combined neural network model , 2005, Pattern Recognit..

[6]  Paul S Addison,et al.  Wavelet transforms and the ECG: a review , 2005, Physiological measurement.

[7]  Michael G. Strintzis,et al.  ECG pattern recognition and classification using non-linear transformations and neural networks: A review , 1998, Int. J. Medical Informatics.

[8]  Madhuchhanda Mitra,et al.  QRS Complex detection using Empirical Mode Decomposition based windowing technique , 2010, 2010 International Conference on Signal Processing and Communications (SPCOM).

[9]  A. Dliou,et al.  Arrhythmia ECG Signal Analysis using Non Parametric Time-Frequency Techniques , 2012 .

[10]  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.

[11]  B. A. Harvey,et al.  Neural network-based EKG pattern recognition , 2002 .

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

[13]  Joerg F. Hipp,et al.  Time-Frequency Analysis , 2014, Encyclopedia of Computational Neuroscience.

[14]  R. Rangayyan,et al.  Biomedical Signal Analysis , 2015 .

[15]  Alan Murray,et al.  Estimation of the ECG signal spectrum during ventricular fibrillation using the fast Fourier transform and maximum entropy methods , 1993, Proceedings of Computers in Cardiology Conference.

[16]  Mj Martin Bastiaans.  Time-frequency signal analysis , 2008 .

[17]  Rashid Saleem,et al.  Using Reduced Interference Distribution to Analyze Abnormal Cardiac Signal , 2011 .

[18]  Boualem Boashash,et al.  Adaptive instantaneous frequency estimation of multi-component FM signals , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[19]  Qiang Fang,et al.  Time-frequency analysis of normal and abnormal biological signals , 2006, Biomed. Signal Process. Control..

[20]  Patrick Flandrin,et al.  Time-Frequency/Time-Scale Analysis , 1998 .

[21]  William J. Williams,et al.  Improved time-frequency representation of multicomponent signals using exponential kernels , 1989, IEEE Trans. Acoust. Speech Signal Process..

[22]  Mohammed A. Al-Manie.  Deconvolution Methods for Biomedical Signals Analysis , 2010 .

[23]  Rangaraj M. Rangayyan,et al.  Biomedical Signal Analysis: A Case-Study Approach , 2001 .

[24]  W. Hueston,et al.  Management of common arrhythmias: Part I. Supraventricular arrhythmias. , 2002, American family physician.

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

[26]  Boualem Boashash,et al.  IF estimation for multicomponent signals , 2003 .

[27]  WeiFang Zhu,et al.  A new QRS detector based on empirical mode decomposition , 2010, IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS.

[28]  L. Cohen,et al.  Time-frequency distributions-a review , 1989, Proc. IEEE.

[29]  Eugene N. Bruce,et al.  Biomedical Signal Processing and Signal Modeling , 2000 .