Efficient procedure to remove ECG from sEMG with limited deteriorations: Extraction, quasi-periodic detection and cancellation

Abstract

The interpretations of the surface electromyography (sEMG) signals from the trunk region are strongly distorted by the heart activity (ECG), especially in case of low-amplitude EMG responses analyses. Many methods have been investigated to resolve this nontrivial problem, by using advanced data processing on the overall sEMG recorded signal. However, if they reduce ECG artifacts, those cancellation methods also deteriorate noiseless parts of the signal. This work proposes an original ECG cancellation method designed to limit the deterioration of sEMG information. To do that, the proposed techniques does not directly attempt to remove the ECG, but is based on two main steps: the localization of ECG and the cancellation of ECG but only where heart pulses have been detected. The phase of localization efficiently extracts the ECG contribution by combining the discrete wavelet transforms (DWT) and the method of independent component analysis (ICA). And finally, this phase takes advantage of quasi-periodic properties of ECG signals to accurately detect pulses localization with an original algorithm based on the fast Fourier transform (FFT). Intensive simulations were achieved in terms of relative errors, coherence and accuracy for different levels of ECG interference. And the correlation coefficients computed from the paraspinal muscles EMG signals of 12 healthy participants were also used to evaluate the developed method. The results from simulation and real data demonstrate that the proposed method accurately detects pulses positions and efficiently removes the ECG from EMG signals, even when both signals are strongly overlapped, and greatly limits the deterioration of the EMG.

Evaluation of the Brain Function State During Mild Cognitive Impairment Based on Weighted Multiple Multiscale Entropy

“…The signals were filtered using the FieldTrip toolbox with a band frequency of 0.5-40 Hz. Different kinds of noise such as the power frequency, electro-oculogram (EOG) (Geetha and Geethalakshmi, 2012), electro-myogram (EMG) (Mortezaee et al, 2019), electro-cardiogram (ECG) (Nougarou et al, 2018), and galvanic skin response (GSR) (Sun et al, 2019) were removed in EEG signals. The statistical computations were performed using statistical product and service solutions (SPSS).…”

Essential Motor Cortex Signal Processing: an ERP and functional connectivity MATLAB toolbox -- user guide version 2.0

“…removing the ECG artifact, can be processed through several different simple or more complex approaches [37]. High-pass frequency filters (HPF) [38], ECG subtraction through QRS-complex detection (FAS) [39], adaptive filtering (AF) approaches [40,41], ICA based approaches [42,43] and also combined AF-ICA based methods [44]. Here in this toolbox however, we promise to stick with the simple and fast methods and thus, implement a modified high-pass frequency filtering approach not only to gain from the simplicity but to also improve the performance accuracy of removing ECG artifact from EMG signals.…”

Classification of Myoelectric Signal using Spectrogram Based Window Selection

“…However, the recognition of EMG signals is usually difficult due to the complex nature of the signal itself [6]. Additionally, EMG signals often contaminated by the heart electrical activity, motion artifact and noise [7], [8]. In such case, signal processing is preferred for analyzing the EMG signals.…”