Respiratory and motion artefacts removal from ICG signal using denoising techniques for hemodynamic parameters monitoring

Abstract

The impedance cardiography (ICG) is a reliable, non-invasive method widely used in clinical practice for the measurement of a multitude of hemodynamic parameters for the diagnosis of cardiovascular disease and continuous monitoring. Signal processing field is necessary to eliminate noises as an artefact of respiration and movement, to extract features characteristics from ICG signals. This paper discusses the concept of wavelet denoising based on scale-dependent thresholding, which is used in two types of the orthogonal wavelet family: Daubechies wavelets (db) and Symlet (sym) applied to the ICG. The study is based on wavelet coefficients that are thresholded using Sureshrink, NeighBlock, and classical thresholds such as Rigrsure and Sqtwolog; they are all compared with linear filters as well as with the LMS-based adaptive filtering algorithm already implemented in biosignal denoising. The results of the evaluation of the performance parameters show that the best denoising technique that gives good results in noise reduction is that of sym8 wavelets at level 5, and the most optimal thresholding technique is the Rigrsure technique with a mean error rate (MER) equal to 0.0001%. The proposed method has shown the reliability of results that can help us later to extract precisely significant information to diagnose earlier and monitor cardiovascular disorders.