A weighted bio-signal denoising approach using empirical mode decomposition

Abstract

Purpose The purpose of this study is to show the effectiveness of a physiological signal denoising approach called EMD-DWT-CLS. Methods This paper presents a new approach for signal denoising based on empirical mode decomposition (EMD), discrete wavelet transform (DWT) thresholding, and constrained least squares (CLS). In particular, the noisy signal is decomposed by empirical mode decomposition (EMD) to obtain intrinsic mode functions (IMFs) plus a residue. Then, each IMF is denoised by using the discrete wavelet transform (DWT) thresholding technique. Finally, the denoised signal is recovered by performing a weighted summation of the denoised IMFs except the residue. The weights are determined by estimating a constrained least squares coefficients; where, the sum of the coefficients is constrained to unity. We used human ECG and EEG signals, and also two EEG signals from left and right cortex of two healthy adult rats. Results The 36 experimental results show that the proposed EMD-DWT-CLS provides higher signal-to-noise ratio (SNR) and lower mean of squared errors (MSE) than the classical EMD-DWT model. Conclusions Based on comparison with classical EMD-DWT model used in the literature, the proposed approach was found to be effective in human and animal physiological signals denoising.

A Machine Learning Prediction Model for Automated Brain Abnormalities Detection

“…The EMD was considered as an adaptive technique for decomposing the ECG signals. Lahmiri and Boukadoum [20] utilized a weighted bio-signal denoising approach for the analysis of ECG and EEG signals. In this paper, different tech-niques such as Discrete Wavelet Transformation (DWT), Empirical Mode Decomposition (EMD) and Constrained Least Square (CLS) models were utilized to increase the detection rate.…”

“…This tool is also used to know the cause of unconsciousness in comatose patients [3]. Spectral information of EEG signal can be obtained by focusing on the frequency bands, namely, Alpha waves (8)(9)(10)(11)(12), Beta waves (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), Gamma waves (above 30 Hz), Theta waves (4)(5)(6)(7)(8) and Delta waves (1)(2)(3)(4). These enable easy understanding for an accurate diagnosis of the classifications as mentioned above, signifing a different mental state of a patient.…”

Estimation of Brain Connectivity during Motor Imagery Tasks using Noise-Assisted Multivariate Empirical Mode Decomposition

“…MIbased BCI systems can be a novel interaction option for those with motor disabilities because they do not require voluntary muscle control [4]. The physiological basis for such an MI paradigm is the mu (8)(9)(10)(11)(12) and beta rhythms (18)(19)(20)(21)(22)(23)(24)(25) in the EEG, which are found in the motor cortex region of the brain when subjects imagine movement of their hands or fingers [5]. Currently, bandpass filters, such as infinite impulse response (IIR) filters, are often used to extract the power features in the frequency bands relevant to MI tasks [1].…”

“…In this paper, we investigate the empirical mode decomposition (EMD) algorithm [10] to address the nonstationary and nonlinear neuronal signals, which is a fully data-driven time-frequency analysis algorithm. There is no prior assumption regarding the data in the process of EMD, making it suitable for the analysis of EEG [12].…”

Recent advances in biomagnetism and its applications

“…Development of methods for removing noises and artifacts is of great importance in the field of biomedical signal processing [6,7]. Biomagnetic recordings including MEG and magnetocardiography (MCG) also suffer from large environmental electromagnetic noises, and thus they often require special signal processing methods such as signal space separation (SSS) [8] and independent component analysis (ICA) [9].…”