Impedance cardiography signal denoising using discrete wavelet transform

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.

Automatic analysis of pre‐ejection period during sleep using impedance cardiogram

“…Defining an index that quantifies the pattern similarity between the ICG cycles and a reference template can solve such issues and will be addressed in a future work. Unlike artifact reduction techniques based on ensemble averaging (Cieslak et al, 2015;Kelsey & Guethlein, 1990;Muzi et al, 1985;Qu et al, 1986;Riese et al, 2003;Shoemaker et al, 1988) and digital filtering (Bagal et al, 2018;Chabchoub et al, 2016;Wang et al, 1995) that are based on the assumption that a valid ICG signal always exists and is only affected by noise and artifacts to a certain extent, our proposed artifact rejection algorithm provided a reliable measure of the level of noise and artifacts that can be used for the detection and exclusion of highly corrupted ICG cycles that cannot be recovered. Invalid ICG data such as those due to electrode detachment (see Figure Figure 1b,d) cannot be recovered by any artifact reduction algorithm.…”

“…Automatic algorithms based on ensemble averaging (Cieslak et al, 2015;Kelsey & Guethlein, 1990;Muzi et al, 1985;Qu, Zhang, Webster, & Tompkins, 1986;Riese et al, 2003;Shoemaker, Appel, Kram, Nathan, & Thompson, 1988) and digital filtering (Bagal, Pandey, Naidu, & Hardas, 2018;Chabchoub, Mansouri, & Salah, 2016;Nagel et al, 1989;Wang, Sun, & Van de Water, 1995;Yamamoto et al, 1988) have been used to reduce the effect of noise and artifacts on the ICG signal. However, such algorithms are only applicable at moderate levels of noise and artifacts when valid ICG cycles still exist and are recoverable.…”

Novel characterization method of impedance cardiography signals using time-frequency distributions

“…Nevertheless, this work takes into account that such characteristics are often partially unknown, and the proposed methodology has been applied to ICG signals. On the other hand, some authors have used wavelet transforms for denoising ICG signals and for locating the characteristic points of the ICG curve [ 11 , 12 , 48 , 49 ]. The main difference is that wavelet analysis uses some given analyzing wavelets (such as the so-called Mexican hat or Morlet wavelets) while the objective of this work was to investigate the overall time-frequency content of the ICG signals.…”

“…Several hemodynamic indices can be extracted from ICG signal [ 6 , 7 ], and applications range from stroke volume calculation to diagnosis of cardiac or work-related conditions [ 8 – 10 ]. In order to improve the calculation of these indices, several authors have exploited the periodic or quasi-periodic behavior of the ICG signals for denoising or for locating their characteristic points [ 11 , 12 ]. The study of these signals in the frequency domain can shed light on the quasi-periodical behavior of ICG signals and also on ICG features which cannot be directly observed in the time domain.…”

Diagnosis of mitral insufficiency using impedance cardiography technique ICG

“…Thus, an ICG signal denoising step is necessary to ensure better results for this present study. ICG signal denoising details have been published previously [42].…”