Adaptive filtering for ECG rejection from surface EMG recordings

Removing ECG noise from surface EMG signals using adaptive filtering

Surface electromyograms (EMGs) are valuable in the pathophysiological study and clinical treatment for dystonia. These recordings are critically often contaminated by cardiac artefact. Our objective of this study was to evaluate the performance of an adaptive noise cancellation filter in removing electrocardiogram (ECG) interference from surface EMGs recorded from the trapezius muscles of patients with cervical dystonia. Performance of the proposed recursive-least-square adaptive filter was first quantified by coherence and signal-to-noise ratio measures in simulated noisy EMG signals. The influence of parameters such as the signal-to-noise ratio, forgetting factor, filter order and regularization factor were assessed. Fast convergence of the recursive-least-square algorithm enabled the filter to track complex dystonic EMGs and effectively remove ECG noise. This adaptive filter procedure proved a reliable and efficient tool to remove ECG artefact from surface EMGs with mixed and varied patterns of transient, short and long lasting dystonic contractions.

Effect of electrocardiographic contamination on surface electromyography assessment of back muscles

The purpose of this study was to demonstrate the relative effect of electrocardiography (ECG) on back muscle surface electromyography (SEMG) parameters and their corresponding sensitivity in low back pain (LBP) assessment. Back muscle SEMG activities were recorded from 17 healthy subjects and 18 chronic LBP patients under static postures (straight sitting and upright standing), and dynamic action (flexion-extension). ECG cancellation based on independent component analysis (ICA) method was performed. Root mean square (RMS) and median frequency (MF) of raw and denoised SEMG data were computed respectively. Multiple comparisons were then performed. A consistent trend of change (increased MF and decreased RMS) followed ECG removal was noticed. In particular, in SEMG measurements under static postures, a significant decrease in RMS (p<0.05) and increase in MF (p<0.05) were found in all recording muscle groups. Level of corruption by ECG artifacts on SEMG measurements was found to be more serious and prominent in static postures than that in dynamic action. After ECG removal, significant improvements in the ability of SEMG to discriminate LBP patients from healthy subjects were seen in RMS amplitude recorded while standing (p<0.05) and MF in all measuring conditions (p<0.05). This study provides a more complete understanding on the relative effect of ECG contamination on back muscles SEMG parameters and LBP assessment.

Real time ECG artifact removal for myoelectric prosthesis control

The electrocardiogram (ECG) artifact is a major noise source contaminating the electromyogram (EMG) of torso muscles. This study investigates removal of ECG artifacts in real time for myoelectric prosthesis control, a clinical application that demands speed and efficiency. Three methods with simple and fast implementation were investigated. Removal of ECG artifacts by digital high-pass filtering was implemented. The effects of the cutoff frequency and filter order of high-pass filtering on the resulting EMG signal were quantified. An alternative adaptive spike-clipping approach was also developed to dynamically detect and suppress the ECG artifacts in the signal. Finally, the two methods were combined. Experimental surface EMG recordings with different ECG/EMG ratios were used as testing signals to evaluate the proposed methods. As a key parameter for clinical myoelectric prosthesis control, the average rectified amplitude of the signal was used as the performance indicator to quantitatively analyze the EMG content distortion and the ECG artifact suppression imposed by the two methods. Aiming at clinical application, the optimal parameter assignment for each method was determined on the basis of the performance using the suite of testing signals with various ECG/EMG ratios.

Development of the system to detect and process Electromyogram signals

This paper is about the design of a system that can detect four channel electromyogram (EMG) signals. EMG signals can be acquired, transformed into electrical signals and input into computer through AD acquisition card. The computer then processes the data and the signal figures are displayed on the screen. In the signals processing system various methods integrated such as adaptive filtering, the methods in time domain, the methods for frequency analysis, and also some newly developed methods, e.g., wavelet transform. The nonlinear tools such as the recurrence quantification analysis (RQA) also has been introduced to analyze the surface EMG and has shown particularly promising for the detection of muscle statue changes. The basic construction of the system and the software analysis are introduced.

Eliminating cardiac contamination from myoelectric control signals developed by targeted muscle reinnervation

The electrocardiogram (ECG) artifact is a major noise contaminating the myoelectric control signals when using shoulder disarticulation prosthesis. This is an even more significant problem with targeted muscle reinnervation to develop additional myoelectric sites for improved prosthesis control in a bilateral amputee at shoulder disarticulation level. This study aims at removal of ECG artifacts from the myoelectric prosthesis control signals produced from targeted muscle reinnervation. Three ECG artifact removal methods based on template subtracting, wavelet thresholding and adaptive filtering were investigated, respectively. Surface EMG signals were recorded from the reinnervated pectoralis muscles of the amputee. As a key parameter for clinical myoelectric prosthesis control, the amplitude measurement of the signal was used as a performance indicator to evaluate the proposed methods. The feasibility of the different methods for clinical application was also investigated with consideration of the clinical speed requirements and memory limitations of commercial prosthesis controllers.

Carmeli's S index assesses motion and muscle artefact reduction in rowers' electrocardiograms

Electrocardiograms (ECG) suffer high perturbations from motion and muscle activities when measured on the chest of rowers. However, for cardiac assessment and sport performance measures, ECG complexes and their time occurrences contain information of great value. We thus propose to use electromyogram (EMG) sensors placed on the distal pectoral muscle to provide a reference signal for motion and muscle activity cancellation. The low frequencies of the EMG signal are conserved for motion cancellation. A combination of band-pass and adaptive filters allows us to recover ECG complexes. This specific scheme is intended to be used for advanced ECG sensors with on-board digital signal processing which requires low-power electronics. A validation of the method is performed based on a nonlinear dynamic model of the ECG and linear frequency modulated rowing movement for various input and output signal-to-noise ratios and adaptive filter gain parameters. Results show a good motion tracking cancellation of the adaptive filter. Finally, assessments of the method on the real recorded data from five subjects are presented. To this end, we have first computed estimates of the signal-to-noise ratio gain between the original and noise-reduced signals. Second, we have developed a modified synchronization index first proposed by Carmeli et al between a QRS template and the original and noise-reduced signals. Both assessment measures are complementary to each other.

Adaptive filtering of ECG interference on surface EEnGs based on signal averaging

An external electroenterogram (EEnG) is the recording of the small bowel myoelectrical signal using contact electrodes placed on the abdominal surface. It is a weak signal affected by possible movements and by the interferences of respiration and, principally, of the cardiac signal. In this paper an adaptive filtering technique was proposed to identify and subsequently cancel ECG interference on canine surface EEnGs by means of a signal averaging process time-locked with the R-wave. Twelve recording sessions were carried out on six conscious dogs in the fasting state. The adaptive filtering technique used increases the signal-to-interference ratio of the raw surface EEnG from 16.7 +/- 6.5 dB up to 31.9 +/- 4.0 dB. In addition to removing ECG interference, this technique has been proven to respect intestinal SB activity, i.e. the EEnG component associated with bowel contractions, despite the fact that they overlap in the frequency domain. In this way, more robust non-invasive intestinal motility indicators can be obtained with correlation coefficients of 0.68 +/- 0.09 with internal intestinal activity. The method proposed here may also be applied to other biological recordings affected by cardiac interference and could be a very helpful tool for future applications of non-invasive recordings of gastrointestinal signals.

Elimination of electrocardiogram contamination from electromyogram signals: An evaluation of currently used removal techniques

Trunk electromyographic signals (EMG) are often contaminated with heart muscle electrical activity (ECG) due to the proximity of the collection sites to the heart and the volume conduction characteristics of the ECG through the torso. Few studies have quantified ECG removal techniques relative to an uncontaminated EMG signal (gold standard or criterion measure), or made direct comparisons between different methods for a given set of data. Understanding the impacts of both untreated contaminated EMG and ECG elimination techniques on the amplitude and frequency parameters is vital given the widespread use of EMG. The purpose of this study was to evaluate four groups of current and commonly used techniques for the removal of ECG contamination from EMG signals. ECG recordings at two intensity levels (rest and 50% maximum predicted heart rate) were superimposed on 11 uncontaminated biceps brachii EMG signals (rest, 7 isometric and 3 isoinertial levels). The 23 removal methods used were high pass digital filtering (finite impulse response (FIR) using a Hamming window, and fourth-order Butterworth (BW) filter) at five cutoff frequencies (20, 30, 40, 50, and 60 Hz), template techniques (template subtraction and an amplitude gating template), combinations of the subtraction template and high pass digital filtering, and a frequency subtraction/signal reconstruction method. For muscle activation levels between 10% and 25% of maximum voluntary contraction, the template subtraction and BW filter with a 30 Hz cutoff were the two best methods for maximal ECG removal with minimal EMG distortion. The BW filter with a 30 Hz cutoff provided the optimal balance between ease of implementation, time investment, and performance across all contractions and heart rate levels for the EMG levels evaluated in this study.