Sample entropy analysis of surface EMG for improved muscle activity onset detection against spurious background spikes

Voluntary surface electromyogram (EMG) signal is sometimes contaminated by spurious background spikes of both physiological and extrinsic or accidental origins. A novel method of muscle activity onset detection against such spurious spikes was proposed in this study based primarily on the sample entropy (SampEn) analysis of the surface EMG. The method takes advantage of the nonlinear properties of the SampEn analysis to distinguish voluntary surface EMG signals from spurious background spikes in the complexity domain. To facilitate muscle activity onset detection, the SampEn analysis of surface EMG was first performed to highlight voluntary EMG activity while suppressing spurious background spikes. Then, a SampEn threshold was optimized for muscle activity onset detection. The performance of the proposed method was examined using both semi-synthetic and experimental surface EMG signals. The SampEn based methods effectively reduced the detection error induced by spurious background spikes and achieved improved performance over the methods relying on conventional amplitude thresholding or its extended version in the Teager Kaiser Energy domain.     

EMG signals detection and processing for on-line control of functional electrical stimulation.

The surface EMG signal detected from voluntarily activated muscles can be used as a control signal for functional neuromuscular electrical stimulation. A proper positioning of the recording electrodes in relation to the stimulation electrodes, and a proper processing of the recorded signals is required to reduce the stimulus artefact and the non-voluntary contribution (M-wave). Six orientations and six locations of the recording electrodes were investigated in the present work. A comb filter (with and without a blanking windowing) was applied to remove the signal components synchronously correlated to the stimulus. An operative definition of the signal to noise ratio and an efficiency index were implemented. It resulted that when the recording electrodes were located within the two stimulation electrodes the best orientation was perpendicular to the longitudinal line. However the best absolute indexes were obtained when the recording electrodes were located externally of the stimulation electrodes, and in that case the best orientation was longitudinal. Concerning the filtering procedure, the use of a blanking window before the application of the comb filter, gave the best performance.     

Trunk muscle onset and cessation in golfers with and without low back pain

The knowledge of the onset and cessation timing of the paraspinal muscles that surround the lumbar spine is an important area of research for the understanding of low back pain. This study examined the timing of the erector spinae and external oblique muscle activity in a group of golfers with and without low back pain. The study compared the results of surface electromyography measurements for two groups of golfers. Twelve male golfers who had reported a mild or greater level of pain in the lower back that was experienced while playing golf were examined. A further fifteen male golfers who had reported no history of lower back pain in the previous 12 months were recruited as controls. The results showed that the low-back-pain golfers switched on their erector spinae muscle significantly in advance of the start of the backswing. This finding was not evident in the group who did not have low back pain symptoms. Low-back-pain golfers, therefore, may use the erector spinae muscle as a primary spinal stabiliser instead of the stronger deeper muscles such as transversus abdominis and multifidus. These results may have important implications for conditioning programmes for golfers with low back pain.     

Decomposition of indwelling EMG signals.

Decomposition of indwelling electromyographic (EMG) signals is challenging in view of the complex and often unpredictable behaviors and interactions of the action potential trains of different motor units that constitute the indwelling EMG signal. These phenomena create a myriad of problem situations that a decomposition technique needs to address to attain completeness and accuracy levels required for various scientific and clinical applications. Starting with the maximum a posteriori probability classifier adapted from the original precision decomposition system (PD I) of LeFever and De Luca (25, 26), an artificial intelligence approach has been used to develop a multiclassifier system (PD II) for addressing some of the experimentally identified problem situations. On a database of indwelling EMG signals reflecting such conditions, the fully automatic PD II system is found to achieve a decomposition accuracy of 86.0% despite the fact that its results include low-amplitude action potential trains that are not decomposable at all via systems such as PD I. Accuracy was established by comparing the decompositions of indwelling EMG signals obtained from two sensors. At the end of the automatic PD II decomposition procedure, the accuracy may be enhanced to nearly 100% via an interactive editor, a particularly significant fact for the previously indecomposable trains.     

Adaptive filtering for ECG rejection from surface EMG recordings.

Surface electromyograms (EMG) of back muscles are often corrupted by electrocardiogram (ECG) signals. This noise in the EMG signals does not allow to appreciate correctly the spectral content of the EMG signals and to follow its evolution during, for example, a fatigue process. Several methods have been proposed to reject the ECG noise from EMG recordings, but seldom taking into account the eventual changes in ECG characteristics during the experiment. In this paper we propose an adaptive filtering algorithm specifically developed for the rejection of the electrocardiogram corrupting surface electromyograms (SEMG). The first step of the study was to choose the ECG electrode position in order to record the ECG with a shape similar to that found in the noised SEMGs. Then, the efficiency of different algorithms were tested on 28 erector spinae SEMG recordings. The best algorithm belongs to the fast recursive least square family (FRLS). More precisely, the best results were obtained with the simplified formulation of a FRLS algorithm. As an application of the adaptive filtering, the paper compares the evolutions of spectral parameters of noised or denoised (after adaptive filtering) surface EMGs recorded on erector spinae muscles during a trunk extension. The fatigue test was analyzed on 16 EMG recordings. After adaptive filtering, mean initial values of energy and of mean power frequency (MPF) were significantly lower and higher respectively. The differences corresponded to the removal of the ECG components. Furthermore, classical fatigue criteria (increase in energy and decrease in MPF values over time during the fatigue test) were better observed on the denoised EMGs. The mean values of the slopes of the energy-time and MPF-time linear relationships differed significantly when established before and after adaptive filtering. These results account for the efficacy of the adaptive filtering method proposed here to denoise electrophysiological signals.     

Application of EMG signals for controlling exoskeleton robots.

Exoskeleton robots are mechanical constructions attached to human body parts, containing actuators for influencing human motion. One important application area for exoskeletons is human motion support, for example, for disabled people, including rehabilitation training, and for force enhancement in healthy subjects. This paper surveys two exoskeleton systems developed in our laboratory. The first system is a lower-extremity exoskeleton with one actuated degree of freedom in the knee joint. This system was designed for motion support in disabled people. The second system is an exoskeleton for a human hand with 16 actuated joints, four for each finger. This hand exoskeleton will be used in rehabilitation training after hand surgeries. The application of EMG signals for motion control is presented. An overview of the design and control methods, and first experimental results for the leg exoskeleton are reported.     

Respiratory muscle activity measured with a noninvasive EMG technique: technical aspects and reproducibility.

A new method is being developed to investigate airway obstruction in young children by means of noninvasive electromyography (EMG) of diaphragmatic and intercostal muscles. The purpose of this study was to evaluate the reproducibility of the EMG measurements. Eleven adults, 39 school children (20 healthy, 19 asthmatic), and 16 preschool children were studied during tidal breathing on separate occasions: two for adults with a time interval of 3 wk and three for children with time intervals of 1 and 24 h. Single electrodes were placed on the second intercostal space left and right of the sternum and at the height of the frontal and the dorsal diaphragm. Bipolar electrode pairs were placed on the rectus abdominis muscle. A newly designed digital physiological amplifier without any analog filtering was used to measure the EMG signals. Except for the average dorsal diaphragm EMG derivation in healthy school children on the second occasion, a significant correlation between the mean peak-to-peak inspiratory activity of average diaphragmatic and intercostal EMG was found in the different age groups on the different measurement occasions (P < 0.05). To assess the repeatability, we described the agreement between the repeated measurements within the same subjects. No significant differences were found between the measurements on the separate occasions. Our observations indicate that the EMG signals derived from the diaphragm and intercostal muscles are, in different age groups with and without asthma, reproducible during tidal breathing.     

A novel approach for removing ECG interferences from surface EMG signals using a combined ANFIS and wavelet

In recent years, the removal of electrocardiogram (ECG) interferences from electromyogram (EMG) signals has been given large consideration. Where the quality of EMG signal is of interest, it is important to remove ECG interferences from EMG signals. In this paper, an efficient method based on a combination of adaptive neuro-fuzzy inference system (ANFIS) and wavelet transform is proposed to effectively eliminate ECG interferences from surface EMG signals. The proposed approach is compared with other common methods such as high-pass filter, artificial neural network, adaptive noise canceller, wavelet transform, subtraction method and ANFIS. It is found that the performance of the proposed ANFIS–wavelet method is superior to the other methods with the signal to noise ratio and relative error of 14.97 dB and 0.02 respectively and a significantly higher correlation coefficient (p < 0.05).     

Predicting muscle forces in gait from EMG signals and musculotendon kinematics

An EMG-driven muscle model for determining muscle force-time histories during gait is presented. The model, based on Hill's equation (1938), incorporates morphological data and accounts for changes in musculotendon length, velocity, and the level of muscle excitation for both concentric and eccentric contractions. Musculotendon kinematics were calculated using three-dimensional cinematography with a model of the musculoskeletal system. Muscle force-length-EMG relations were established from slow isokinetic calibrations. Walking muscle force-time histories were determined for two subjects. Joint moments calculated from the predicted muscle forces were compared with moments calculated using a linked segment, inverse dynamics approach. Moment curve correlations ranged from r = 0.72 to R = 0.97 and the root mean square (RMS) differences were from 10 to 20 Nm. Expressed as a relative RMS, the moment differences ranged from a low of 23% at the ankle to a high of 72% at the hip. No single reason for the differences between the two moment curves could be identified. Possible explanations discussed include the linear EMG-to-force assumption and how well the EMG-to-force calibration represented excitation for the whole muscle during gait, assumptions incorporated in the muscle modeling procedure, and errors inherent in validating joint moments predicted from the model to moments calculated using linked segment, inverse dynamics. The closeness with which the joint moment curves matched in the present study supports using the modeling approach proposed to determine muscle forces in gait.     

Trunk muscle coactivation in preparation for sudden load.

Biomechanical stability of the lumbar spine is an important factor in the etiology and control of low-back disorders. A principle component of biomechanical stability is the musculoskeletal stiffening generated by preparatory muscle coactivation. The goal of this investigation was to quantify preparatory behavior, evaluating trunk muscle activity immediately prior to sudden trunk flexion loading during static extension tasks compared to activity observed when subjects were informed no sudden load would occur. Coactive excitation was also examined as a function of fatigue and gender. Results demonstrated increased extensor muscle and flexor muscle coactivation following static fatiguing exertions, potentially compensating for reduced trunk stiffness. Female subjects produced greater flexor antagonism than in the males. No difference in the preparatory coactive muscle recruitment patterns were observed when subjects were expecting a sudden flexion load compared to recruitment patterns observed in similar static postures when subjects were informed no sudden load would be applied. This indicates the neuromuscular system relies greatly on response characteristics for the maintenance of stability in dynamic loading conditions.     

An Improved SSA-Based Technique for EMG Removal from ECG

Background

The electrocardiogram (ECG) signals provide important information about the heart electrical activities in medical and diagnostic applications. This signal may be contaminated by different types of noises. One of the noise types which has a considerable overlap with the ECG signals in frequency domain is electromyogram (EMG). Among the exciting approaches for de-noising the ECG signals, those based on singular spectrum analysis (SSA) are popular.

Gender effects on trapezius surface EMG during delayed onset muscle soreness due to eccentric shoulder exercise.

The purpose of this study was to investigate gender-specific motor control strategies during eccentric exercise and delayed onset muscle soreness (DOMS) in the shoulder region. Twelve healthy males and females participated in the study. Eccentric shoulder exercises were conducted on the dominant shoulder while the other side served as control. The exerted force, range of shoulder elevation, rating of perceived exertion, pain intensity, and surface electromyography (EMG) from the trapezius muscles were recorded and analyzed. A significant decrease in exerted force during exercise was only found in males despite similar rating of perceived exertion among genders. During eccentric exercise: males showed increasing root mean square (RMS) of the EMG while a decrease occurred for females, no difference between genders in mean power frequency of the EMG were seen. During static and dynamic contractions: no differences between genders in pain intensity or RMS were observed; RMS of the exercised side were lower than that of the control side (P<0.05) at 24 h after exercise. The results indicated a more prominent muscle fatigue resistance in females compared with males and mobilization of different muscle activation strategies during eccentric exercise. A protective adaptation to DOMS, i.e. decrease in RMS values was found with no gender differences.     

Relationship between EMG signals and force in human vastus lateralis muscle using multiple bipolar wire electrodes.

This paper describes the relationship between knee extension force and EMG signals detected by multiple bipolar wire electrodes inserted into the human vastus lateralis muscle under isometric conditions. Six healthy male volunteers participated in this study. Eight pairs of bipolar wire electrodes were inserted into the right vastus lateralis muscle and the EMG data were simultaneously detected and analyzed. The EMG raw data and individual force-IEMG relations were influenced by the location of the electrode inserted into the muscle. The force and IEMG relationship averaged across subjects detected from the eight electrodes, however, showed almost the same linear correlation in spite of different electrode locations. No linear correlation was observed between MdF and the knee extension force. This result suggests that, if all of the muscle fibers participate in the same action at the same time, the averaged normalized IEMG from any places using wire electrodes could reflect the total activities of that muscle even if the muscle is large.     

Changes in EMG signals for the muscle tibialis anterior while running barefoot or with shoes resolved by non-linearly scaled wavelets

The purpose of this project was to study the EMG pattern of the tibialis anterior muscle in heel-toe running. Specifically, EMG changes in time, intensity and frequency shortly before and after heel-strike were addressed using an EMG-specific non-linearly scaled wavelets analysis. This method allowed extracting the time, intensity and frequency information inherent in the EMG signal at any time. The EMG signals of 40 male subjects were recorded for running barefoot and with shoes. The results confirmed that the pre-heel-strike EMG activities were typically seen at higher EMG frequencies (60-270Hz) while the post-heel-strike EMG activities resulted in lower frequency signals (10-90Hz). The timing of the pre-heel-strike EMG activities was not influenced by the used shoe conditions. The timing of the post-heel-strike EMG activities was significantly delayed when wearing shoes. The intensity of the pre-heel-strike muscle activity increased compared to the post-heel-strike one when wearing shoes. One can conclude that the activity of the tibialis anterior adjusts specifically to exterior conditions. The frequency shift between pre- and post heel-strike muscle activity were discussed with respect to activation of different motor units.     

Real-time intelligent pattern recognition algorithm for surface EMG signals

Background  

Electromyography (EMG) is the study of muscle function through the inquiry of electrical signals that the muscles emanate. EMG signals collected from the surface of the skin (Surface Electromyogram: sEMG) can be used in different applications such as recognizing musculoskeletal neural based patterns intercepted for hand prosthesis movements. Current systems designed for controlling the prosthetic hands either have limited functions or can only be used to perform simple movements or use excessive amount of electrodes in order to achieve acceptable results. In an attempt to overcome these problems we have proposed an intelligent system to recognize hand movements and have provided a user assessment routine to evaluate the correctness of executed movements.     

Digital filter design for peak detection of surface EMG.

A simple Low-Pass Differential (LPD) filter is often used for the Motor Unit Action Potential (MUAP) peak detection in needle EMG decomposition. Decomposition of surface EMG is much more difficult since surface EMG is more mixed than needle EMG. A successful peak detection of MUAPs is a first important step for EMG decomposition. We found that a simple LPD filter is not suitable for surface MUAP detection; instead the Weighted Low-Pass Differential (WLPD) filters are proposed. The filter performance is analyzed based on different window selection and varying MUAPs from simulated and recorded surface EMG. The sinusoidal WLPD filter is found to have better SNR improving factors, and to be more robust under the varying conditions.