Removal of artifacts from electrocardiogram

The electrocardiogram is the recording of the electrical potential of heart versus time. The analysis of ECG signal has great importance in the detection of cardiac abnormalities. The electrocardiographic signals are often contaminated by noise from diverse sources. Noises that commonly disturb the basic electrocardiogram are power line interference, instrumentation noise, external electromagnetic field interference, noise due to random body movements and respirational movements. These noises can be classified according to their frequency content. It is essential to reduce these disturbances in ECG signal to improve accuracy and reliability. Different types of adaptive and non-adaptive digital filters have been proposed to remove these noises. In this thesis, window based FIR filters, adaptive filters and wavelet filter bank are applied to remove the noises. Performances of the filters are compared based on the PSNR values. It is difficult to apply filters with fixed filter coefficients to reduce the instrumentation noise, because the time varying behaviour of this noise is not exactly known. Adaptive filter technique is required to overcome this problem, as the filter coefficients can be varied to track the dynamic variations of the signals. In wavelet transform, a signal is analyzed and expressed as a linear combination of the summation of the product of the wavelet coefficients and mother wavelet. The wavelet decomposition offers an excellent resolution both in time and frequency domain. Better estimation of the amplitudes is also obtained in wavelet based denoising.

[1]  Richard Adamec,et al.  ECG Holter: Guide to Electrocardiographic Interpretation , 2008 .

[2]  Martin Vetterli,et al.  Wavelets: approximation and compression--a review , 1999, Defense, Security, and Sensing.

[3]  K. M. Lewis,et al.  Sensible Analysis of the 12-Lead ECG , 2000 .

[4]  Ravinder K. Mittal,et al.  Adaptive cancellation of ECG artifacts in the diaphragm electromyographic signals obtained through intraoesophageal electrodes during swallowing and inspiration , 1994 .

[5]  Ching-Haur Chang,et al.  Cancellation of high-frequency noise in ECG signals using adaptive filter without external reference , 2010, 2010 3rd International Conference on Biomedical Engineering and Informatics.

[6]  Roger G. Mark,et al.  The MIT-BIH Arrhythmia Database on CD-ROM and software for use with it , 1990, [1990] Proceedings Computers in Cardiology.

[7]  J.P. Marques de Sa,et al.  ECG noise filtering using wavelets with soft-thresholding methods , 1999, Computers in Cardiology 1999. Vol.26 (Cat. No.99CH37004).

[8]  L. Tarassenko,et al.  One-pass training of optimal architecture auto-associative neural network for detecting ectopic beats , 2001 .

[9]  Emmanuel Ifeachor,et al.  Digital Signal Processing: A Practical Approach , 1993 .

[10]  J. Catalano.  Guide to ECG analysis , 1993 .

[11]  S. C. Chan,et al.  On the design and implementation of FIR and IIR digital filters with variable frequency characteristics , 2002 .

[12]  Brij N. Singh,et al.  Optimal selection of wavelet basis function applied to ECG signal denoising , 2006, Digit. Signal Process..

[13]  Lionel Tarassenko,et al.  Application of independent component analysis in removing artefacts from the electrocardiogram , 2006, Neural Computing & Applications.

[14]  O. Rioul,et al.  Wavelets and signal processing , 1991, IEEE Signal Processing Magazine.

[15]  Tomas B. Garcia,et al.  12-Lead ECG: The Art of Interpretation , 2013 .

[16]  John G. Proakis,et al.  Digital Signal Processing: Principles, Algorithms, and Applications , 1992 .

[17]  A. Ahmadian,et al.  ECG Feature Extraction Based on Multiresolution Wavelet Transform , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[18]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[19]  Hong Wanl,et al.  The Elimination of 50 Hz Power Line Interference from ECG Using a Variable Step Size LMS Adaptive Filtering Algorithm , 2006 .

[20]  Antonio Albiol,et al.  A new adaptive scheme for ECG enhancement , 1999, Signal Process..

[21]  Allan Kardec Barros,et al.  independent , 2006, Gumbo Ya Ya.

[22]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Lionel H. Opie,et al.  Heart Physiology: From Cell to Circulation , 2003 .

[24]  U. RajendraAcharya.  Advances in cardiac signal processing , 2007 .

[25]  Paul Dan Cristea,et al.  High resolution ECG filtering using adaptive Bayesian wavelet shrinkage , 1998, Computers in Cardiology 1998. Vol. 25 (Cat. No.98CH36292).

[26]  R.M. Rangayyan,et al.  Adaptive cancellation of muscle contraction interference in vibroarthrographic signals , 1994, IEEE Transactions on Biomedical Engineering.

[27]  K. L. Yadav.  Performance Evaluation Of Different Adaptive Filters For ECG Signal Processing , 2010 .

[28]  N. B. Jones,et al.  Digital Signal Processing: Principles, Devices and Applications , 1990 .

[29]  M. I. Gabriel Khan,et al.  Rapid ECG Interpretation , 1997 .

[30]  G. Moody,et al.  QRS morphology representation and noise estimation using the Karhunen-Loeve transform , 1989, [1989] Proceedings. Computers in Cardiology.

[31]  Ingrid Daubechies,et al.  The wavelet transform, time-frequency localization and signal analysis , 1990, IEEE Trans. Inf. Theory.

[32]  M. Hoher,et al.  De-noising of high-resolution ECG signals by combining the discrete wavelet transform with the Wiener filter , 1998, Computers in Cardiology 1998. Vol. 25 (Cat. No.98CH36292).

[33]  V Kumar,et al.  QRS detection using new wavelets , 2002, Journal of medical engineering & technology.

[34]  U. C. Pati,et al.  Removal of artifacts from electrocardiogram using digital filter , 2012, 2012 IEEE Students' Conference on Electrical, Electronics and Computer Science.

[35]  Kangshun Li,et al.  The FIR window function design based on evolutionary algorithm , 2011, 2011 International Conference on Mechatronic Science, Electric Engineering and Computer (MEC).

[36]  A. Katz.  Physiology of the heart , 1977 .

[37]  Rafi Ahamed Shaik,et al.  Efficient sign based normalized adaptive filtering techniques for cancelation of artifacts in ECG signals: Application to wireless biotelemetry , 2011, Signal Process..