Abstract
Impedance cardiography (ICG) is a non-invasive technique for diagnosing cardiovascular diseases. In the acquisition procedure, the ICG signal is often affected by several kinds of noise which distort the determination of the hemodynamic parameters. Therefore, doctors cannot recognize ICG waveform correctly and the diagnosis of cardiovascular diseases became inaccurate. The aim of this work is to choose the most suitable method for denoising the ICG signal. Indeed, different wavelet families are used to denoise the ICG signal. The Haar, Daubechies (db2, db4, db6, and db8), Symlet (sym2, sym4, sym6, sym8) and Coiflet (coif2, coif3, coif4, coif5) wavelet families are tested and evaluated in order to select the most suitable denoising method. The wavelet family with best performance is compared with two denoising methods: one based on Savitzky–Golay filtering and the other based on median filtering. Each method is evaluated by means of the signal to noise ratio (SNR), the root mean square error (RMSE) and the percent difference root mean square (PRD). The results show that the Daubechies wavelet family (db8) has superior performance on noise reduction in comparison to other methods.
Similar content being viewed by others
References
Kubicek WG, Karnegis JN, Patterson RP, Witsoe DA, Mattson RH (1966) Development and evaluation of an impedance cardiac output system. Aerosp Med 37:1208–1212
Cybulski G, Strasz A, Niewiadomski W, Gąsiorowska A (2012) Impedance cardiography: recent advancements. Cardiol J 19(5):550–556
Patterson RP (2010) Impedance cardiography: what is the source of the signal? In: International conference on electrical bioimpedance. J Phys: Conf Ser 224: 012118
Lababid Z, Ehmke DA, Durnin RE, Leaverton PE, Lauer RM (1970) The first derivative thoracic impedance cardiogram, american heart association. Circulation 41:651–658
Pandey VK, Pandey PC, Burkule NJ, Subramanyan LR (2011) Adaptive filtering for suppression of respiratory artifact in impedance cardiography. 33rd annual international conference of the IEEE EMBS, Boston
Hu X, Chen X, Ren R, Zhou B, Qian Y, Li H, Xia S (2014) Adaptive filtering and characteristics extraction for impedance cardiography. J Fiber Bioeng Inf 7(1):81–90
Dromer O, Alata O, Bernard O (2009) Impedance cardiography filtering using scale fourier linear combiner based on RLS algorithm. IEEE-EMBC, pp 6930–6933
Pandey VK, Pandey PC (2007) Wavelet based cancellation of respiratory artifacts in impedance cardiography. In: Proceedings of the 2007 15th international conference on digital signal processing (IEEE/DSP), 2007
Pandey VK, Pandey PC (2009) Wavelet based denoising for suppression of motion artifacts in impedance cardiography. In: Proceedings of the international symposium on emerging areas in biotechnology & bioengineering. Mumbai
Sebastian T, Pandey PC, Naidu SMM, Pandey VK (2011) Wavelet based denoising for suppression of respiratory and motion artifacts in impedance cardiography. Comput Cardiol 38:501–504
De Ridder S, Neyt X, Pattyn N, Migeotte P-F (2011) Comparison between EEMD, wavelet and FIR denoising: influence on event detection in impedance cardiography. In: 33rd Annual International Conference of the IEEE EMBS. Boston
Choudhari PC, Panse MS (2015) denoising of radial bioimpedance signals using adaptive wavelet packet transform and kalman filter. IOSR J VLSI Signal Process (IOSR-JVSP) 5(1): e-ISSN: 2319–4200, p-ISSN No.: 2319–4197
Hargittai S (2005) Savitzky–Golay least-squares polynomial filters in ecg signal processing. Comput Cardiol 32:763–766
Awal MA, Mostafa SS, Ahmad M (2011) Performance analysis of Savitzky-Golay smoothing filter using ECG signal, IJCIT, ISSN 2078-5828(Print), ISSN 2218-5224 (Online), 01(02)
Addison PS (2005) Wavelet transforms and the ECG: a review. Physiol Meas 26:R155–R199
Cohen L (1986) Time-frequency distributions—a review. Proc IEEE 77(7):941–981
Hlawatsch F, Boudreaux-Bartels GF (1992) Linear and quadratic time-frequency signal representations. IEEE Signal Process Mag 9(2):21–67
Shoeb A, Clifford G (2005) Chapter 16—wavelets; multiscale activity in physiological signals. Biomed Signal Image Process, Spring
Rioul O, Vetterli M (1991) Wavelets and signal processing. IEEE SP Magazine, pp 14–38
Ben Salah R, Marrakchi A, Ellouze N (1989) Cardiac diseases quantification of by temporal and cepstral analysis of plethysmographic signal. J Islam Acad Sci 2(3):204–211
Luo J, Ying K, He P, Bai J (2005) Properties of Savitzky-Golay digital differentiators. Digit Signal Proc 15:122–136
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Chabchoub, S., Mansouri, S. & Salah, R.B. Impedance cardiography signal denoising using discrete wavelet transform. Australas Phys Eng Sci Med 39, 655–663 (2016). https://doi.org/10.1007/s13246-016-0460-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13246-016-0460-z