JBiSE  Vol.5 No.12 , December 2012
Wavelet diagnosis of ECG signals with kaiser based noise diminution
ABSTRACT
The evaluation of distortion diagnosis using Wavelet function for Electrocardiogram (ECG), Electroencephalogram (EEG) and Phonocardiography (PCG) is not novel. However, some of the technological and economic issues remain challenging. The work in this paper is focusing on the reduction of the noise interferences and analyzes different kinds of ECG signals. Furthermore, a physiological monitoring system with a programming model for the filtration of ECG is presented. Kaiser based Finite Impulse Response (FIR) filter is used for noise reduction and identification of R peaks based on Peak Detection Algorithm (PDA). Two approaches are implemented for detecting the R peaks; Amplitude Threshold Value (ATV) and Peak Prediction Technique (PPT). Daubechies wavelet transform is applied to analyze the ECG of driver under stress, arrhythmia and sudden cardiac arrest signals. From the obtained results, it was found that the PPT is an effective and efficient technique in detecting the R peaks compared to ATV.

Cite this paper
Chandramouleeswaran, S. , Haidar, A. and Samsuri, F. (2012) Wavelet diagnosis of ECG signals with kaiser based noise diminution. Journal of Biomedical Science and Engineering, 5, 705-714. doi: 10.4236/jbise.2012.512088.
References
[1]   Saritha, C., Sukanya, V. and Murthy, Y.N. (2008) ECG signal analysis using wavelet transforms. Journal of Chemical Physics, 35, 68-77.

[2]   Narayana, K.V.L. and Rao, A.B. (2001) Wavelet based QRS detection in ECG using MATLAB. Innovative Systems Design and Engineering, 2, 2222-1727.

[3]   Bakul, G. and Tiwary, U.S. (2010) Automated risk identification of myocardial infarction using relative frequency band coefficient (RFBC) features from ECG. The Open Biomedical Engineering Journal, 4, 217-222. doi:10.2174/1874120701004010217

[4]   Luo, S. and Johnston, P. (2010) A review of electrocar-diogram filtering. Journal of Electrocardiology, 43, 486- 496.

[5]   Lee, J.-W. and Lee, G.-K. (2005) Design of an adaptive filter with a dynamic structure for ECG signal processing. International Journal of Control, Automation, and Systems, 3, 137-142.

[6]   Mbachu, C.B., Onoh, G.N., Idigo, V.E., Ifeagwu, E.N. and Nnebe, S.U. (2011) Processing ECG signal with Kaiser Window-based FIR digital filters. International Journal of Engineering Science and Technology, 3, 6775- 6783.

[7]   Mahesh, S.C., Agarwala, R.A. and Uplane, M.D. (2008) Suppression of baseline wander and power line interference in ECG using digital IIR filter. International Journal of Circuits, Systems and Signal Processing, 2, 356- 365.

[8]   Leonardo, V.B., Elmar, U.K.M. and Luis, C.C. (2001) Compression of ECG signals by optimized quantization of discrete cosine transform coefficients. Medical Engineering & Physics, 23, 127-134. doi:10.1016/S1350-4533(01)00030-3

[9]   Manikandan, M.S. and Dandapat, S. (2006) Wavelet threshold based ECG compression using USZZQ and Huffman coding of DSM. Biomedical Signal Processing and Control, 1, 261-270. doi:10.1016/j.bspc.2006.11.003

[10]   Haque, K.M.F., Ali, Md.H., Kiber, M.A. and Hasan, Md.T. (2009) Detection of small variations of ECG features using Wavelet. ARPN Journal of Engineering and Applied Sciences, 4, 27-30.

[11]   Zoltan, G.-S. and Petru, M. (2007) Wavelet transform based ECG signal denoising. Scientific International Conference, Romania, 15-16 November 2007, 1-7.

[12]   Abdel-Rahman, A.-Q. and Khaled, D. (2010) ECG signal enhancement using wavelet transform. WSEAS Transactions on Biology and Biomedicine, 7, 62-72.

[13]   Manikandan, M.S. and Soman, K.P. (2012) A novel method for detecting R-peaks in electrocardiogram (ECG) signal. Biomedical Signal Processing and Control, 7, 118- 128.

[14]   Salwa, A.K., Mostafa, N.E.-S., Tolba, A.S., Abdel-kader, F.M. and Hisham M.E. (2010) Wavelet packets-based blind watermarking for medical image management. The Open Biomedical Engineering Journal, 4, 93-98.

[15]   Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, P.Ch., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K. and Stanley, H.E. PhysioBank. Physi- oToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signal. http://physionet.ph.biu.ac.il/physiobank/database/mitdb/

[16]   Manikandan, M.S. and Dandapat, S. (2007) Wavelet energy based diagnostic distortion measure for ECG. Biomedical Signal Processing and Control, 2, 80-96. doi:10.1016/j.bspc.2007.05.001

[17]   John, D.E. and Joseph, D.B. (2011) Introduction to biomedical Engineering. 3rd Edition, Academic Press, Waltham, 2011.

 
 
Top