Myoelectrical Signals Research Articles

The Posterior Insular Cortex is Necessary for Feeding-Induced Jejunal Myoelectrical Activity in Male Rats

The gastrointestinal tract exhibits coordinated muscle motility in response to food digestion, which is regulated by the central nervous system through autonomic control. The insular cortex is one of the brain regions that may regulate the muscle motility. In this study, we examined whether, and how, the insular cortex, especially the posterior part, regulates gastrointestinal motility by recording jejunal myoelectrical signals in response to feeding in freely moving male rats. Feeding was found to induce increases in jejunal myoelectrical signal amplitudes. This increase in the jejunal myoelectrical signals was abolished by vagotomy and pharmacological inhibition of the posterior insular cortex. Additionally, feeding induced a decrease and increase in sympathetic and parasympathetic nervous activities, respectively, both of which were eliminated by posterior insular cortical inhibition. These results suggest that the posterior insular cortex regulates jejunal motility in response to feeding by modulating autonomic tone.

Spectral characterization of human leg EMG signals from an open access dataset for the development of computational models.

Large-scale neuromusculoskeletal models have been used for predicting mechanisms underlying neuromuscular functions in humans. Simulations of such models provide several types of signals of practical interest, such as surface electromyographic signals (EMG), which are compared with experimental data for interpretations of neurophysiological phenomena under study. Specifically, realistic characterization of spectral properties of simulated EMG signals is important for achieving powerful inferences, whereas considerations should be taken for myoelectric signals of different muscles. In this study, we characterized spectral properties of surface interference pattern EMG signals and motor unit action potentials (MUAP) acquired from three plantar flexor muscles: Soleus (SO), Medial Gastrocnemius (MG), and Lateral Gastrocnemius (LG); and one dorsiflexor muscle: Tibialis Anterior (TA). Surface EMG signals were acquired from 20 participants using the same convention for electrode placement. Specifically, interference pattern EMG signals were obtained during isometric constant force contractions at 5%, 10% and 20% of maximum voluntary contraction (MVC), whereas surface MUAPs were decomposed from surface EMG signals obtained at low contraction forces. We compared the spectrum median frequency (MDF) estimated from interference pattern EMG signals across muscles and contraction intensities. Additionally, we compared MDF and durations of MUAPs between muscles. Our results showed that MDF of interference pattern EMG signals acquired from TA were higher compared to SO, MG, and LG for all contraction intensities i.e., 5%, 10%, and 20% MVC. Consistently, MUAPs acquired from TA also had higher MDF values and shorter durations compared to the other leg muscles. We provide herein a dataset with the surface MUAPs waveforms and interference pattern EMG signals obtained for this study, which should be useful for implementing and validating the simulation of myoelectrical signals of leg muscles. Importantly, these results indicate that spectral properties of myoelectrical signals should be considered for improving EMG modeling in large-scale neuromusculoskeletal models.

Assist-as-needed control with a soft robotic glove based on human-object contact estimation

Assist-as-needed control with a soft robotic hand glove for active rehabilitation is studied in this work. There are two resources of the grasping force, the robotic glove and the subject. Compared with traditional passive rehabilitation where the grasping force is merely provided by a robotic hand rehabilitation device (such as hand exoskeleton, robotic glove), assist-as-needed control accounts for the user contribute to performing grasping tasks collaboratively. In this control method, the human muscle strength for grasping is estimated through the myoelectrical signals of the human forearm collected by the MYO armband. A neural network is used for the recognition of human-object contact estimation. The assist-as-needed control is finally implemented to assist humans in grasping tasks. Experiment results on a soft robotic glove show the effectiveness of the proposed assistive control method.

Light-GBM based signal correction method for surface myoelectropotential measured by multi-channel band-type EMG sensor

The aim of this research is to develop a user interface using myoelectrical signals, a type of biological signal. A multi-channel band-type EMG sensor is used to measure this signal. The goal is to make it easier to attach and detach the EMG sensor, while still obtaining sufficiently accurate measurements even if the sensor is placed slightly off the standard position. To achieve this, a Light-GBM based method is proposed to estimate the degree of deviation of the EMG sensor from the standard position. Once the deviation is estimated, a filter is constructed to compensate for the effect of the deviation on the myoelectrical signal. Experimental results using this method show that the sensor misalignment can be estimated with over 90% accuracy, and that correcting the myoelectrical signal based on this information improves joint angle estimation by approximately 13% in terms of root mean square error.

A new approach to preprocessing of EMG signal to assess the correctness of muscle condition

Purpose: Electromyography (EMG) is a technique dealing with the recording and analysis of myoelectric signals formed by physiological variations in the muscle fiber membrane. The voltage potential of surface myoelectrical signals (sEMG) varies over time and depends on the characteristics of the individual subject. One of the main drawbacks of sEMG analysis is that the given acquisition conditions strongly determine the amplitude of the signal. The analysis of sEMG requires appropriate preprocessing, including proper filtering and artifact removal. Moreover, the sEMG data must be converted to a scale standardized for all measurements. This research aimed both to propose a method of sEMG processing to eliminate the occurring disturbances, in particular impulsive artifacts, and to determine the level of muscle excitation based on normalized sEMG. The analysis of muscle excitation level can be applied to assess muscle activity during physical activity. Design/methodology/approach: The proposed algorithm uses set of digital filters, probabilistic distribution and the decomposition of the sEMG signal to attenuate artifacts. Variance analysis of the sEMG derivative is used to determine muscle excitation. The sEMG signals were acquired with the VICON system with the sampling frequency set at 1000 Hz, and processed in MATLAB. During sEMG recordings, standard silver/silver chloride (Ag/AgCl) surface electrodes were used. Findings: The suggested technique was validated using sEMG recorded for eight persons during deep squat. Normalized excitation was determined for the left and right muscles, the rectus femoris, the vastus medialis, and the biceps femoris. Obtained outcomes indicate a possibility to assess the correctness of muscles condition. Originality/value: The combination of the proposed filter and the analysis variance-based thresholding method can effectively eliminate impulse artifacts within the surface myoelectrical signals. Keywords: Spikes disturbance, EMG, Muscle excitation, Filtering, Kurtosis. Category of the paper: Research paper.

Accurate Continuous Prediction of 14 Degrees of Freedom of the Hand from Myoelectrical Signals through Convolutive Deep Learning.

Natural control of assistive devices requires continuous positional encoding and decoding of the user's volition. Human movement is encoded by recruitment and rate coding of spinal motor units. Surface electromyography provides some information on the neural code of movement and is usually decoded into finger joint angles. However, the current approaches to mapping the electrical signal into joint angles are unsatisfactory. There are no methods that allow precise estimation of joint angles during natural hand movements within the large numbers of degrees of freedom of the hand. We propose a framework to train a neural network from digital cameras and high-density surface electromyography from the extrinsic (forearm and wrist) hand muscles. Furthermore, we show that our 3D convolutional neural network optimally predicted 14 functional flexion/extension joints of the hand. We found in our experiments (4 subjects; mean age of 26±2.12 years) that our model can predict individual sinusoidal finger movement at different speeds (0.5 and 1.5 Hz), as well as two and three finger pinching, and hand opening and closing, covering 14 degrees of freedom of the hand. Our deep learning method shows a mean absolute error of 2.78±0.28 degrees with a mean correlation coefficient between predicted and expected joint angles of 0.94, 95% confidence interval (CI) [0.81, 0.98] with simulated real-time inference times lower than 30 milliseconds. These results demonstrate that our approach is capable of predicting the user's volition similar to digital cameras through a non-invasive wearable neural interface. Clinical relevance- This method establishes a viable interface that can be used for both immersive virtual reality medical simulations environments and assistive devices such as exoskeleton and prosthetics.

Gastrointestinal Myoelectric Measurements via Simultaneous External and Internal Electrodes in Pigs

IntroductionCurrently, there is no accurate noninvasive measurement system to diagnose gastrointestinal (GI) motility disorders. Wireless skin patches have been introduced to provide an accurate noninvasive measurement of GI myoelectric activity which is essential for developing neuro-stimulation devices to treat GI motility disorders. The aim of this study is to compare the external and internal electrical signal measurements in ambulatory pigs. MethodsYucatan pigs underwent placement of internal electrodes on the stomach, small intestine, and colon. Wires were brought through the abdominal wall. Signals were collected by a wireless receptor. Four external patches were placed on the abdominal skin to record the signals simultaneously. Pigs were kept for 6 d while the sensors were continuously recording the data from both systems. ResultsInternal sensors detected rich signals from each organ. The stomach had a dominant frequency that ranged from 4 to 4.5 cpm, with occasional higher frequencies at 2, 3 and 4 times that. Small intestine signals had their primary energy in the 12-15 cpm range. Colon signals primarily displayed a dominant broad peak in the 4-6 cpm region. External skin patches detected a substantial fraction of the activities measured by the internal electrodes. A clear congruence in the frequency spectrum was observed between the internal and external readings. ConclusionsInternally measured myoelectrical signals confirmed different patterns of rhythmic activity of the stomach, small intestine, and colon. Skin patches provided GI myoelectric measurement with a range of frequencies that could be useful in the diagnosis and treatment of motility disorders.

Hydrophilic, Breathable, and Washable Graphene Decorated Textile Assisted by Silk Sericin for Integrated Multimodal Smart Wearables

AbstractAchieving integrated systems with comfortability and durability comparable to traditional textiles is one of the ultimate pursuits of smart wearables. This work reports a hydrophilic, breathable, biocompatible, and washable graphene‐decorated electronic textile that is achieved with the assistance of silk sericin and enables the fabrication of comfortable and integrated multisensing textiles. The graphene‐decorated textile is prepared by dyeing commercial textile with an aqueous graphene ink, which contains natural silk sericin‐coated graphene and is free of any artificial chemicals. The conformally coated hydrophilic sericin–graphene flakes and the well‐reserved knitted structure endow the textile with good conductivity, excellent hydrophilicity, biocompatibility, breathability, and flexibility, ensuring its electronic performance and wearing‐comfort. Moreover, the obtained textile is washable after processed with a cross‐linking agent. Based on the obtained textile, an integrated multisensing textile that can simultaneously collect and analyze myoelectrical and mechanical signals is developed, enabling the recognition and distinguishment of complex human motions. With the combined features of hydrophilicity, breathability, biocompatibility, washability, and versatility, this strategy of fabricating electronic textiles based on conventional textiles and an aqueous sericin–graphene ink provides a scalable and sustainable way to construct smart wearables.

Design of a non-invasive sensing system for diagnosing gastric disorders

Gastric disorders are widely spread among the population of any age. At the moment, the diagnosis is made by using invasive systems that cause several side effects. The present manuscript proposes an innovative non-invasive sensing system for diagnosing gastric dysfunctions. The Electro-GastroGraphy (EGG) technique is used to record myoelectrical signals of stomach activities. Although EGG technique is well known for a long time, several issues concerning the signal processing and the definition of suitable diagnostic criteria are still unresolved. So, EGG is to this day a trial practice. The authors want to overcome the current limitations of the technique and improve its relevance. To this purpose, a smart EGG sensing system has been designed to non-invasively diagnose gastric disorders. In detail, the system records the gastric slow waves by means of skin surface electrodes placed in the epigastric area. Cutaneous myoelectrical signals are so acquired from the body surface in proximity of stomach. Electro-gastrographic record is then processed. According to the diagnostic model designed from the authors, the system estimates specific diagnostic parameters in time and frequency domains. It uses Discrete Wavelet Transform to obtain power spectral density diagrams. The frequency and power of the EGG waveform and the dominant frequency components are so analyzed. The defined diagnostic parameters are put in comparison with the reference values of a normal EGG in order to estimate the presence of gastric pathologies by the analysis of arrhythmias (<em>tachygastria</em>, <em>bradygastria</em> and irregular rhythm). The paper aims to describe the design of the system and of the arrhythmias detection algorithm. Prototype development and experimental data will be presented in future works. Preliminary results show an interesting relevance of the suggested technique so that it can be considered as a promising non-invasive tool for diagnosing gastrointestinal motility disorders.

Pilot Validation of a New Wireless Patch System as an Ambulatory, Noninvasive Tool That Measures Gut Myoelectrical Signals: Physiologic and Disease Correlations.

Limited means exist to assess gastrointestinal activity in a noninvasive, objective way that is highly predictive of underlying motility disorders. The aim of this paper is to demonstrate the feasibility of recording myoelectric gastrointestinal activity by cutaneous patches and to correlate myoelectric signals with gastrointestinal function in various clinical settings. A novel wireless patch system (WPS) (G-Tech Medical) that acquires gastrointestinal myoelectrical signals was placed on the patients' anterior abdomens. Data were transmitted wirelessly to a mobile device with a user interface and forwarded to a cloud server where processing algorithms identified episodes of motor activity, quantified their parameters, and nominally assigned them to specific gastrointestinal organs based on their frequencies. The inherent reproducibility of the WPS measurement technique itself and from the underlying gut activity, coupled with source validation and sensitivity to changes in gut activity in several physiologic and pathologic states, demonstrates its feasibility, safety, and performance in clinical settings. The novel WPS technology, measuring myoelectric intestinal activity noninvasively and continuously over multiple days, is feasible in a wide range of clinical settings, highlighting its promise in the diagnosis and management of motility disorders. Further research is required for more extensive validation and to determine how best to employ this information to optimize patient care.

Monitoring gastric myoelectric activity after pancreaticoduodenectomy for diet "readiness".

Postoperative delayed gastric emptying (DGE) is a frustrating complication of pancreaticoduodenectomy (PD). We studied whether monitoring of postoperative gastric motor activity using a novel wireless patch system can identify patients at risk for DGE. Patients ( n = 81) were prospectively studied since 2016; 75 patients total were analyzed for this study. After PD, battery-operated wireless patches (G-Tech Medical) that acquire gastrointestinal myoelectrical signals are placed on the abdomen and transmit data by Bluetooth. Patients were divided into early and late groups by diet tolerance of 7 days [enhanced recovery after surgery (ERAS) goal]. Subgroup analysis was done of patients included after ERAS initiation. The early and late groups had 50 and 25 patients, respectively, with a length of stay (LOS) of 7 and 11 days ( P < 0.05). Nasogastric insertion was required in 44% of the late group. Tolerance of food was noted by 6 versus 9 days in the early versus late group ( P < 0.05) with higher cumulative gastric myoelectrical activity. Diminished gastric myoelectrical activity accurately identified delayed tolerance to regular diet in a logistical regression analysis [area under the curve (AUC): 0.81; 95% confidence interval (CI), 0.74-0.92]. The gastric myoelectrical activity also identified a delayed LOS status with an AUC of 0.75 (95% CI, 0.67-0.88). This stomach signal continued to be predictive in 90% of the ERAS cohort, despite earlier oral intake. Measurement of gastric activity after PD can distinguish patients with shorter or longer times to diet. This noninvasive technology provides data to identify patients at risk for DGE and may guide the timing of oral intake by gastric "readiness." NEW & NOTEWORTHY Limited clinical indicators exist after pancreaticoduodenectomy to allow prediction of delayed gastric emptying (DGE). This study introduces a novel, noninvasive, wireless patch system capable of accurately monitoring gastric myoelectric activity after surgery. This system can differentiate patients with longer or shorter times to a regular diet as well as provide objective data to identify patients at risk for DGE. This technology has the potential to individualize feeding regimens based on gastric activity patterns to improve outcomes.

Monitoring gastric myoelectric after pancreaticoduodenectomy for diet "readiness"

Background: Delayed gastric emptying (DGE) is a frustrating complication of pancreaticoduodenectomy (PD). Limited clinical indicators immediately after PD allow for prediction of DGE. This study is to assess whether monitoring of postoperative gastric motor activity after PD can identify patients at risk for DGE. Methods: Sixty-five patients were studied since April 2016. After PD, battery-operated wireless patches (GutCheck, G-Tech Medical) that acquire gastric myoelectrical signals are placed on the anterior abdomen and transmit data by Bluetooth. Patients were divided into EARLY and LATE groups, by regular diet tolerance as of 7 days (cut off based on enhanced recovery after surgery (ERAS) goal). A subgroup analysis was done of the first 10 patients included after initiation of the ERAS protocol in May 2017. Results: The EARLY and LATE groups had 41 and 24 patients, respectively, with length of stay 7 and 11 days (p < 0.05). Nasogastric insertion was required in 50% of the LATE group. Tolerance of solid food was noted by 6 days vs 9 days in the EARLY vs LATE group (p < 0.05). Gastric activity was notably higher in the EARLY vs LATE group (Figure); this stomach signal continued to be predictive in the ERAS cohort despite earlier initiation of oral intake. Gastric myoelectrical activity within the first 48 h of surgery was inversely correlated with time to tolerance of regular diet (r = −0.50, p < 0.05). Conclusion: Measurement of gastric activity after PD can distinguish between patients with shorter or longer times to regular diet. Patients in the LATE group had more clinically relevant DGE and weaker stomach signal as early as 2 days postop. Patients who did not meet target ERAS goals also had weaker stomach signals. This technology provides objective data that identifies patients at risk for DGE and may guide timing of oral intake by gastric "readiness."

Spontaneous electrical activities at myofascial trigger points at different stages of recovery from injury in a rat model.

BackgroundSpontaneous electrical activity (SEA) is a feature of myofascial trigger points (MTrPs), which can either be latent or active. However, SEA at different stages of recovery from MTrPs remains unclear.ObjectiveTo investigate the temporal changes in the nature of SEA after generation of MTrPs in a rat model.Methods32 rats were divided into four groups: 24 rats were assigned to experimental groups (EGs), which underwent the MTrP modelling intervention and 8 were allocated to a control group (CG). All EG rats received a blunt strike to the left vastus medialis combined with eccentric exercise for 8 weeks. After modelling, the EG rats were subdivided into three groups with total recovery times of 4, 8 and 12 weeks (EG-4w, EG-8w and EG-12w, respectively). Taut bands (TBs) with and without the presence of active MTrPs were identified in the left hind limb muscles of all rats, verified by SEA and further examined with electromyography recordings. Myoelectrical signals were also categorised into one of five types.ResultsCG rats had fewer TBs than EG rats and EGs showed variable frequencies of SEA. SEA frequencies were higher in EG-4w than in EG-8w and EG-12w groups (240.57±72.9 vs 168.14±64.5 and 151.63±65.4, respectively, p<0.05) and were significantly greater in all EGs than in the CG (55.75±21.9). Relative to CG rats, amplitudes and durations of electrical potentials in the EG were only increased in the EG-8w and EG-12w groups. Types IV and V myoelectrical signals were never seen in latent MTrPs and type V signals did not occur in EG-4w rats.ConclusionsIncreasing recovery periods following a MTrP modelling intervention in rats are characterised by different frequencies and amplitudes of SEA from TBs.Trial registration number2014012.

The GutCheck Part 2: Signal Processing and Preliminary Clinical Utility of Surface-Measured Myoelectrical Signals of the GI Tract

The GutCheck Part 2: Signal Processing and Preliminary Clinical Utility of Surface-Measured Myoelectrical Signals of the GI Tract

The GutCheck Part 1: Signal Acquisition and Analysis of Surface-measured Myoelectrical Signals of the Human GI Tract

The GutCheck Part 1: Signal Acquisition and Analysis of Surface-measured Myoelectrical Signals of the Human GI Tract

The GutCheck Part 3: Early Clinical Utility of Surface-measured Myoelectrical Signals of the GI Tract in Patients With Functional GI Disorders and Healthy Controls

The GutCheck Part 3: Early Clinical Utility of Surface-measured Myoelectrical Signals of the GI Tract in Patients With Functional GI Disorders and Healthy Controls

Dual effects of melatonin on uterine myoelectrical activity of non-pregnant rats

In this experimental study, we aimed to investigate the role of melatonin on uterine myoelectrical activity of non-pregnant rats. Forty-six female rats were assigned to six groups: (1) control; (0.2 mL 0.9% NaCl was injected intravenously (IV), n=6); (2) melatonin applied as 0.4 mg/kg/IV (n=8); (3) melatonin applied as 4 mg/kg/IV (n=8); (4) single dose of oxytocin (100 mU/kg) injected IV (n=8); (5) melatonin (0.4 mg/kg) plus oxytocin (100 mU/kg) (n=8); and (6) melatonin (4 mg/kg) plus oxytocin (100 mU/kg) injected IV (n=8). Each rat underwent a laparotomy, and uterine myoelectrical signals were recorded. The mean spectrum, averaged over the spectral content of signals in each group, was compared. Melatonin induced uterine myoelectrical activity in a dose-dependent manner. Treatment of melatonin after oxytocin suppressed the mean power of the signals. Serum melatonin concentrations were significantly higher in melatonin-treated rats. Melatonin itself at two different dose levels was found to be equally effective in stimulating the uterine electrical signals, although oxytocin-induced uterine electrical activity was suppressed by melatonin. These findings merit further investigations on the possible beneficial role of melatonin in the treatment of conditions associated with abnormal uterine activity.

Development of a Low-cost Upper-limb Myoelectrical Prosthestis and its Open Source Training System

Bioelectrical signals are used more frequently in order to provide an alternative way to command devices, such as myoelectrical signals or electroencephalic signals -the last ones used in Brain-Computer Interface applications-. The goal of this paper is using myoelectrical signals to command a robotic upperlimb prosthesis developed as well. The only requirement to be able to use the prosthesis would be to have a voluntary and accurate muscle control. In that way, a training system for prosthesis' future users has been developed in order to train their muscle control.

Effect of Whole Body Vibration during Static Squats on the Myoelectrical Properties of the Vastus Lateralis

The effects of whole body vibration (WBV) on EM G timing, frequency and amplitude have previously been studied, however to best of the authors knowledge there have been no studies on the effect of WBV on the conduction velocity of myoelectrical signals. Muscle fibre conduction velocity, EM G frequency and amplitude of the vastus lateralis were therefore assessed in twelve male participants (1.81 +/- 0.1 m, 82.2 +/ - 10.7 kg, 25.4 +/- 4.0 years) at the start and end of partial static squats, both with and without WBV (40 Hz 1.9 mm vertical d isplacement). Significant differences were found in the median frequencies, but not in the conduction velocity or signal amplitude. The observed changes are indicative of whole body vibration increasing the vastus lateralis muscle activity but not to a level indicat ive of physiological fat igue. These observations are of use to exercise and rehabilitation practit ioners considering the use of vibration in exercise protocols.

Apoio manual na cintura escapular aumenta a coativação durante o treino de marcha de crianças com paralisia cerebral

Introdução: Não foram encontrados estudos nos quais tenham sido examinadas diferentes estratégias terapêuticas utilizadas como recurso no treino de marcha de crianças com paralisia cerebral. Objetivos: Comparar o nível de atividade e coativação dos músculos tibial anterior e gastrocnêmio medial durante duas estratégias utilizadas no treino de marcha: uma, com uso de andador; e uma, com apoio manual na cintura escapular. Métodos: Foram analisadas cinco crianças com paralisia cerebral. O grau de espasticidade das crianças foi avaliado por meio da Escala de Espasticidade Ashworth Modificada. Os sinais mioelétricos foram analisados por meio da amplitude da atividade eletromiográfica e do índice de coativação. Resultados: Durante a marcha com apoio manual na cintura escapular, houve um significativo aumento (p