Effect of electrocardiographic contamination on surface electromyography assessment of back muscles

Singular Value Decomposition for Removal of Cardiac Interference from Trunk Electromyogram

A new algorithm based on singular value decomposition (SVD) to remove cardiac contamination from trunk electromyography (EMG) is proposed. Its performance is compared to currently available algorithms at different signal-to-noise ratios (SNRs). The algorithm is applied on individual channels. An experimental calibration curve to adjust the number of SVD components to the SNR (0–20 dB) is proposed. A synthetic dataset is generated by the combination of electrocardiography (ECG) and EMG to establish a ground truth reference for validation. The performance is compared with state-of-the-art algorithms: gating, high-pass filtering, template subtraction (TS), and independent component analysis (ICA). Its applicability on real data is investigated in an illustrative diaphragm EMG of a patient with sleep apnea. The SVD-based algorithm outperforms existing methods in reconstructing trunk EMG. It is superior to the others in the time (relative mean squared error < 15%) and frequency (shift in mean frequency < 1 Hz) domains. Its feasibility is proven on diaphragm EMG, which shows a better agreement with the respiratory cycle (correlation coefficient = 0.81, p-value < 0.01) compared with TS and ICA. Its application on real data is promising to non-obtrusively estimate respiratory effort for sleep-related breathing disorders. The algorithm is not limited to the need for additional reference ECG, increasing its applicability in clinical practice.

Assessment of Lumbar Muscles Coordinated Activity Based on High-Density Surface Electromyography: A Pilot Study

Trunk-movement involves coordinated activity of different lumbar muscles. By assessing the lumbar muscles activity, the pathogeny of some neuromuscular disease might be revealed. Surface electromyography (sEMG) could be used to measure the muscle activity, but for assessing lumbar muscles coordinated activity, there lacks of an accurate and comprehensive application of sEMG. High-density (HD) sEMG provides a potential to assess lumbar muscles coordinated activity more accurately. Thus, in this pilot study, the objective was to assess the lumbar muscles coordinated activity based on HD sEMG. By placing a 5×15 array (75 channels) of HD sEMG electrodes to the surface of the low back area, the sEMG signal from four healthy subjects could be collected. In order to analyze the lumbar muscles coordinated activity, the sEMG signal during different trunk-movements was recorded. Through calculating the root-mean-square (RMS) of each channel and interpolating the RMS value between channels, the sEMG topography could be obtained. The high activity area in the topography showed a regular distribution during different trunk-movements. It might be useful for further assessment of lumbar disease such as low back pain.

A Machine Learning-based Surface Electromyography Topography Evaluation for Prognostic Prediction of Functional Restoration Rehabilitation in Chronic Low Back Pain

STUDY DESIGN

A retrospective study.

OBJECTIVE

The aim of this study was to investigate the feasibility and applicability of support vector machine (SVM) algorithm in classifying patients with LBP who would obtain satisfactory or unsatisfactory progress after the functional restoration rehabilitation program.

SUMMARY OF BACKGROUND DATA

Dynamic surface electromyography (SEMG) topography has demonstrated the potential use in predicting the prognosis of functional restoration rehabilitation for patients with low back pain (LBP). However, processing from raw SEMG topography to make prediction is not easy to clinicians.

METHODS

A total of 30 patients with nonspecific LBP were recruited and divided into "responding" and "non-responding" group according to the change of Visual analog pain rating scale and Oswestry Disability Index. Each patient received a 12-week functional restoration rehabilitation program. A normal database was calculated from a control group from 48 healthy participants. Root-mean-square difference (RMSD) was extracted from the recorded dynamic SEMG topography during symmetrical and asymmetrical trunk-movement. SVM and cross-validation were applied to the prediction based on the optimized features selected by the sequential floating forward selection (SFFS) algorithm.

RESULTS

RMSD feature parameters following rehabilitation in the "responding" group showed a significant difference (P < 0.05) with the one in the "nonresponding" group. The SVM classifier with Quadratic kernel based on SFFS-selected features showed the best prediction performance (accuracy: 96.67%, sensitivity: 100%, specificity: 93.75%, average area under curve [AUC]: 0.8925) comparing with linear kernel (accuracy: 80.00%, sensitivity: 85.71%, specificity: 75.00%, average AUC: 0.7825), polynomial kernel (accuracy: 93.33%, sensitivity: 92.86%, specificity: 93.75%, average AUC: 0.9675), and radial basis function (RBF) kernel (accuracy: 86.67%, sensitivity: 85.71%, specificity: 87.50%, average AUC: 0.7900).

CONCLUSION

The use of SVM-based classifier of SEMG topography can be applied to identify the patient responding to functional restoration rehabilitation, which will help the healthcare worker to improve the efficiency of LBP rehabilitation.

LEVEL OF EVIDENCE

3.

Properties of Force Output and Spectral EMG in Young Patients with Nonspecific Low Back Pain during Isometric Trunk Extension

[Purpose] To clarify the influence of nonspecific low back pain (NSLBP) on force fluctuation and the myoelectric data of back muscles during isometric trunk extension at low to high force levels. [Subjects] Fourteen male subjects with NSLBP and 14 healthy male control subjects participated in this study. [Methods] All participants extended their trunk isometrically maintaining 10 levels of target force [2, 5, 10, 15, 20, 30, 50, 70, 80 and 90% of maximal voluntary contraction (MVC) in a random order] for about 4 seconds with visual feedback. A force transducer and tri-axis force sensor were positioned at the 7th thoracic vertebra to measure force output and the direction of force. Myoelectric activities of the back muscles (longissimus thoracis, L2 level; multifidus, S1 level) were recorded by surface electromyography. [Results] Force output of NSLBP subjects fluctuated more than that of healthy subjects at 30% and 50%MVC. Higher median power frequency in the multifidus was observed in NSLBP subjects at moderate to high force levels. [Conclusion] These results show that the properties of force output in NSLBP subjects differ from those in healthy subjects, suggesting that the assessment of force fluctuation of back muscles at moderate force levels is a useful index for evaluating and discriminating NSLBP.

A novel technique for muscle onset detection using surface EMG signals without removal of ECG artifacts

Surface electromyography (EMG) signal from trunk muscles is often contaminated by electrocardiography (ECG) artifacts. This study presents a novel method for muscle activity onset detection by processing surface EMG against ECG artifacts. The method does not require removal of ECG artifacts from raw surface EMG signals. Instead, it applies the sample entropy (SampEn) analysis to highlight EMG activity and suppress ECG artifacts in the signal complexity domain. A SampEn threshold can then be determined for detection of muscle activity. The performance of the proposed method was examined with different SampEn analysis window lengths, using a series of combinations of 'clean' experimental EMG and ECG recordings over a wide range of signal to noise ratios (SNRs) from -10 to 10 dB. For all the examined SNRs, the window length of 128 ms yielded the best performance among all the tested lengths. Compared with the conventional amplitude thresholding and integrated profile methods, the SampEn analysis based method achieved significantly better performance, demonstrated as the shortest average latency or error among the three methods (p < 0.001 for any of the examined SNRs except 10 dB).

Surface electromyography signal processing and classification techniques

Electromyography (EMG) signals are becoming increasingly important in many applications, including clinical/biomedical, prosthesis or rehabilitation devices, human machine interactions, and more. However, noisy EMG signals are the major hurdles to be overcome in order to achieve improved performance in the above applications. Detection, processing and classification analysis in electromyography (EMG) is very desirable because it allows a more standardized and precise evaluation of the neurophysiological, rehabitational and assistive technological findings. This paper reviews two prominent areas; first: the pre-processing method for eliminating possible artifacts via appropriate preparation at the time of recording EMG signals, and second: a brief explanation of the different methods for processing and classifying EMG signals. This study then compares the numerous methods of analyzing EMG signals, in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above.

Reducing electrocardiographic artifacts from electromyogram signals with independent component analysis

The aim of this work was to reduce ECG artifacts from surface electromyogram (EMG) signals collected from lumbar muscles with the blind source separation technique based on independent component analysis (ICA). Using four EMG signals collected above erector spinal lumbar muscles from 27 subjects, the proposed method fail in separating the sources. However, when considering a single channel of EMG and the same one time-shifted by one sample, the FastICA allowed reducing the signal to ECG noise ratio.

Man-machine interface system for neuromuscular training and evaluation based on EMG and MMG signals

This paper presents the UVa-NTS (University of Valladolid Neuromuscular Training System), a multifunction and portable Neuromuscular Training System. The UVa-NTS is designed to analyze the voluntary control of severe neuromotor handicapped patients, their interactive response, and their adaptation to neuromuscular interface systems, such as neural prostheses or domotic applications. Thus, it is an excellent tool to evaluate the residual muscle capabilities in the handicapped. The UVa-NTS is composed of a custom signal conditioning front-end and a computer. The front-end electronics is described thoroughly as well as the overall features of the custom software implementation. The software system is composed of a set of graphical training tools and a processing core. The UVa-NTS works with two classes of neuromuscular signals: the classic myoelectric signals (MES) and, as a novelty, the myomechanic signals (MMS). In order to evaluate the performance of the processing core, a complete analysis has been done to classify its efficiency and to check that it fulfils with the real-time constraints. Tests were performed both with healthy and selected impaired subjects. The adaptation was achieved rapidly, applying a predefined protocol for the UVa-NTS set of training tools. Fine voluntary control was demonstrated to be reached with the myoelectric signals. And the UVa-NTS demonstrated to provide a satisfactory voluntary control when applying the myomechanic signals.

Flexion-relaxation ratio in sitting: application in low back pain rehabilitation

STUDY DESIGN

A multiple-comparative study between normal and low back pain (LBP) patients before and after rehabilitation.

OBJECTIVE

To examine whether there is a change in flexion-relaxation phenomenon in sitting in LBP patient following a rehabilitation treatment.

SUMMARY OF BACKGROUND DATA

There is an association between LBP and seated spine posture. Previous study has reported an absence of flexion-relaxation phenomenon in LBP patients during sitting. However, it is unknown whether there is a difference in flexion-relaxation phenomenon in sitting in LBP patients before and after rehabilitation treatment.

METHODS

A total of 20 normal subjects and 25 chronic LBP patients who underwent a 12 weeks rehabilitation program were recruited. Surface electromyography recordings during upright sitting and flexed sitting were taken from the paraspinal muscles (L3) bilaterally from the normal subjects, and in the LBP patients before and after the rehabilitation treatment. The main outcome measures for patients include the visual analogue scale, Oswestry disability index, subjective tolerance for sitting, standing and walking, trunk muscle endurance, lifting capacity, and range of trunk motion in the sagittal plane. Flexion-relaxation phenomenon in sitting, expressed as a ratio between the average surface electromyography activity during upright and flexed sitting, was compared between normal and patients; and in LBP patients before and after rehabilitation.

RESULTS

Flexion-relaxation ratio in sitting in normal subjects (Left: 6.83 +/- 3.79; Right: 3.45 +/- 2.2) presented a significantly higher (Left: P < 0.001; Right: P < 0.05) value than LBP patients (Left: 3.04 +/- 2.36; Right: 2.02 +/- 1.49). An increase in flexion-relaxation ratio in sitting was observed in LBP patients after rehabilitation (Left: 4.69 +/- 3.94, P < 0.05; Right: 3.58 +/- 2.97, P < 0.001), together with a significant improvement (P < 0.05) in subjective tolerance in sitting and standing, abdominal and back muscle endurance, lifting capacity, and range of motion. There were no significant changes in disability and pain scores, and subjective tolerance in walking.

CONCLUSION

Flexion-relaxation ratio in sitting has demonstrated its ability to discriminate LBP patients from normal subjects, and to identify changes in pattern of muscular activity during postural control after rehabilitation.

Creation of an asymmetrical gradient of back muscle activity and spinal stiffness during asymmetrical hip extension

BACKGROUND

Low back pain is often associated with increased spinal stiffness which thought to arise from increased muscle activity. Unfortunately, the association between paraspinal muscle activity and paraspinal stiffness, as well as the spatial distribution of this relation, is unknown. The purpose of this investigation was to employ new technological developments to determine the relation between spinal muscle contraction and spinal stiffness over a large region of the lumbar spine.

METHODS

Thirty-two male subjects performed graded isometric prone right hip extension at four different exertion levels (0%, 10%, 25% and 50% of the maximum voluntary contraction) to induce asymmetric back muscle activity. The corresponding stiffness and muscle activity over bilateral paraspinal lumbar regions was measured by indentation loading and topography surface electromyography, respectively. Paraspinal stiffness and muscle activity were then plotted and their correlation was determined.

FINDINGS

Data from this study demonstrated the existence of an asymmetrical gradient in muscle activation and paraspinal stiffness in the lumbar spine during isometric prone right hip extension. The magnitude and scale of the gradient increased with the contraction force. A positive correlation between paraspinal stiffness and paraspinal muscle activity existed irrespective of the hip extension effort (Pearson correlation coefficient, range 0.566-0.782 (P<0.001)).

INTERPRETATION

Our results demonstrate the creation of an asymmetrical gradient of muscle activity and paraspinal stiffness during right hip extension. Future studies will determine if alterations in this gradient may possess diagnostic or prognostic value for patients with low back pain.