INT. J. BIOAUTOMATION, 2020, 24(4), 323-336 doi: 10.7546/ijba.2020.24.4.000549 Electrocardiogram Signal Denoising by Hilbert Transform and Synchronous Detection Mohammed Assam Ouali, Asma Tinouna, Mouna Ghanai, Kheireddine Chafaa LASS Laboratory, Electronics Departement, Faculty of Technology University of M’sila, Algeria E-mails: mohamedassam.ouali@univ-msila.dz , asma.tinouna@yahoo.com , mouna.ghanai@mail.univ-batna.dz , kheireddine.chafaa@mail.univ-batna.dz * Corresponding author Received: July 30, 2019 Accepted: May 06, 2020 Published: December 31, 2020 Abstract: An efficient method for Electrocardiogram (ECG) signal denoising based on synchronous detection and Hilbert transform techniques is presented. The goal of the method is to decompose a noisy ECG signal into two components classified according to their energy: (1) component with high energy representing the dominant component which is the clean ECG signal, and (2) component with low energy representing the sub-dominant component which is the contaminant noise. The investigated approach is validated through out some experimentations on MIT-BIH ECG database. Experimental results show that random noises can be effectively suppressed from ECG signals. Keywords: ECG signals, ECG denoising, Hilbert transform, Synchronous detection. Introduction Electrocardiogram (ECG) is a bioelectrical signal, which records the heart’s electrical activity versus time [6, 16, 20]. It is an important diagnostic tool for assessing heart function. The ECG is a time varying signal which is neither periodic nor deterministically chaotic (the interbeat intervals seems to contain a random component). Each phase of cardiac electrical activity pro- duces a specific wave or a complex one. The basic ECG waves are labelled alphabetically as P wave, QRS complex, ST segment and T wave [1]. During ECG measurement, noise (anything other than muscular activity of heart) is superimposed on it, due to AC interference, loose elec- trode connection, malfunctioning of machine, patient movement like respiration etc., all of them collectively called artefacts. Hence, extraction of clean ECG signal from noisy measurements is needed and this is one of the big problems in biomedical signal processing. Latest contributions in this subject are reported in [7–9, 15, 18–21, 23, 25]. In the last few years, many researchers have proposed methods and approaches for ECG sig- nal denoising. Wavelet transform is generally employed for ECG denoising due to its ability to characterize time-frequency domain information of a time domain signal. Yadav et al. [25] have proposed a novel non-local wavelet transform (NLWT) method for ECG signal denoising by ex- ploiting the local and non-local redundancy present in the signal. Smital et al. [18] developed a method using dyadic stationary wavelet transform (SWT) in the Wiener filter for the estimation of a noise-free signal. The number of decomposition levels and the impulse characteristics are the two most important factors considered in SWT. A method based on sparse derivatives (SD) was presented by Ning et al. [15] where the arte- facts are reduced by modeling the clean ECG signal as a sum of two signals whose second and third-order derivatives are sparse respectively. Tracey and Miller [21] suggested using a 323