Electromyogram and EMG

BACKGROUND AND AIMS: Pain and muscle fatigue in the low back and lower extremity associated with adult spinal deformity (ASD) markedly limit daily activities and affect quality of life. This study aimed to clarify if spinal correction surgery decreases the muscle activity requirements in relation to alignment and balance parameters. METHODS: Integrated electromyogram (I-EMG) studies of the low back and lower extremity in addition to whole body alignment, body sway, and health-related quality of life (HRQOL) were evaluated in 16 patients with ASD before and after surgery...

Bradykinesia, or slow movement, is a defining symptom of Parkinson's disease (PD), but the underlying neuromechanical deficits that lead to this slowness remain unclear. People with PD often have impaired rates of motor output accompanied by disruptions in neuromuscular excitation, causing abnormal, segmented, force-time curves. Previous investigations using single-joint models indicate that agonist electromyogram (EMG) silent periods cause motor segmentation. It is unknown whether motor segmentation is evident in more anatomically complex and ecologically important tasks, such as handgrip tasks...

Over recent decades, electroencephalogram (EEG) has become an essential tool in the field of clinical analysis and neurological disease research. However, EEG recordings are notably vulnerable to artifacts during acquisition, especially in clinical settings, which can significantly impede the accurate interpretation of neuronal activity. Blind source separation is currently the most popular method for EEG denoising, but most of the sources it separates often contain both artifacts and brain activity, which may lead to substantial information loss if handled improperly...

Decomposition of EMG signals provides the decoding of motor unit (MU) discharge timings. In this study, we propose a fast gradient convolution kernel compensation (fgCKC) decomposition algorithm for high-density surface EMG decomposition and apply it to an offline and real-time estimation of MU spike trains. We modified the calculation of the cross-correlation vectors to improve the calculation efficiency of the gradient convolution kernel compensation (gCKC) algorithm. Specifically, the new fgCKC algorithm considers the past gradient in addition to the current gradient...

Cerebral palsy (CP) includes a group of neurological conditions caused by damage to the developing brain, resulting in maladaptive alterations of muscle coordination and movement. Estimates of joint moments and contact forces during locomotion are important to establish the trajectory of disease progression and plan appropriate surgical interventions in children with CP. Joint moments and contact forces can be estimated using electromyogram (EMG)-informed neuromusculoskeletal models, but a reduced number of EMG sensors would facilitate translation of these computational methods to clinics...

Continuous Intraoperative Neurophysiologic Monitoring (cIONM) is a widely used technology to improve surgical outcomes and prevent cranial nerve injury during skull base surgery. Monitoring of free-running electromyogram (EMG) plays an important role in cIONM, which can be used to identify different discharge patterns, alert the surgeon to potential nerve damage promptly, etc. In this dataset, we collected clinical multichannel EMG signals from 11 independent patients' data using a Neuromaster G1 MEE-2000 system (Nihon Kohden, Inc...

Balance involves several sensory modalities including vision, proprioception and the vestibular system. This study aims to investigate vestibulospinal activation elicited by tone burst stimulation in various muscles and how head position influences these responses. We recorded electromyogram (EMG) responses in different muscles (sternocleidomastoid-SCM, cervical erector spinae-ES-C, lumbar erector spinae-ES-L, gastrocnemius-G, and tibialis anterior-TA) of healthy participants using tone burst stimulation applied to the vestibular system...

BACKGROUND: Cortico-muscular coherence (CMC) between the cerebral cortex and muscle activity is an effective tool for studying neural communication in the motor control system. To accurately evaluate the coherence between electroencephalogram (EEG) and electromyogram (EMG) signals, it is necessary to accurately calculate the time delay between physiological signals to ensure signal synchronization. NEW METHOD: We proposed a new delay estimation method, named wavelet coherence time lag (WCTL) and the significant increase areas (SIA) index as a measure of the specific region enhancement effect of the magnitude squared coherence (MSC) image...

BACKGROUND: One of the characteristics of parkinsonian tremor is that its amplitude decreases with movement. Current models suggest an interaction between basal ganglia (BG) and cerebello-thalamo-cortical circuits in parkinsonian tremor pathophysiology. OBJECTIVE: We aimed to correlate central oscillation in the BG with electromyographic activity during re-emergent tremor in order to detect changes in BG oscillatory activity when tremor is attenuated by movement...

Sleep research is fundamental to understanding health and well-being, as proper sleep is essential for maintaining optimal physiological function. Here we present SlumberNet, a novel deep learning model based on residual network (ResNet) architecture, designed to classify sleep states in mice using electroencephalogram (EEG) and electromyogram (EMG) signals. Our model was trained and tested on data from mice undergoing baseline sleep, sleep deprivation, and recovery sleep, enabling it to handle a wide range of sleep conditions...

Sleep stage classification is essential for clinical disease diagnosis and sleep quality assessment. Most of the existing methods for sleep stage classification are based on single-channel or single-modal signal, and extract features using a single-branch, deep convolutional network, which not only hinders the capture of the diversity features related to sleep and increase the computational cost, but also has a certain impact on the accuracy of sleep stage classification. To solve this problem, this paper proposes an end-to-end multi-modal physiological time-frequency feature extraction network (MTFF-Net) for accurate sleep stage classification...

This review investigates the convergence of artificial intelligence (AI) and personalized health monitoring through wearable devices, classifying them into three distinct categories: bio-electrical, bio-impedance and electro-chemical, and electro-mechanical. Wearable devices have emerged as promising tools for personalized health monitoring, utilizing machine learning to distill meaningful insights from the expansive datasets they capture. Within the bio-electrical category, these devices employ biosignal data, such as electrocardiograms (ECGs), electromyograms (EMGs), electroencephalograms (EEGs), etc...

OBJECTIVE: electromyogram (EMG)-driven musculoskeletal models have been widely used to investigate human movements while existing EMG-driven models commonly neglect regional heterogeneity in anatomy and activation within a skeletal muscle. To consider neuromuscular compartment anatomy and activation, a subject- and compartment-specific EMG-driven model was developed for isometric plantarflexion moment prediction. METHODS: the model was hill-type consisting of gastrocnemius medialis, gastrocnemius lateralis, and soleus around the ankle joint, and each muscle was discretised into four compartments...

Power assistance on joint torque may not be beneficial to all the related muscles. We investigated the effects of power assistance on torque release during isokinetic elbow flexion. An isokinetic dynamometer system was used to simulate dynamic elbow flexion with power assistance, which altered the exercise conditions of baseline isometric torque (greater and lower) and rotation speed (faster and slower) of the lever arm. Ten male right-handed participants performed exercise tasks using the system. We measured (1) the electromyogram (EMG) amplitudes of the biceps brachii (BB), brachioradialis (BR), and triceps brachii (TB) muscles, (2) torque output and its variability, and (3) the perceived assistance level...

Over the past two decades the importance of trunk contribution to sporting performance has been highlighted through the expanse of literature concerning core stability and strength. However, the role of trunk motion and the abdominal muscles are yet to be established during sprint kayak performance. The purpose of this study was to determine the associations among trunk rotation, kayak velocity, and abdominal muscle activity during on-water sprint kayaking. Eight international paddlers completed five 150 m sprint trials...

Prosthetic devices are vital for enhancing personal autonomy and the quality of life for amputees. However, the rejection rate for electric upper-limb prostheses remains high at around 30%, often due to issues like functionality, control, reliability, and cost. Thus, developing reliable, robust, and cost-effective human-machine interfaces is crucial for user acceptance. Machine learning algorithms using Surface Electromyography (sEMG) signal classification hold promise for natural prosthetic control. This study aims to enhance hand and wrist movement classification using sEMG signals, treated as time series data...

INTRODUCTION: The levels of low-density lipoprotein (LDL) cholesterol in plasma are important risk factors for coronary heart disease. Several reports suggest that elevated plasma cholesterol is associated with cardiac arrhythmias. In a subsequent study investigating LDL cholesterol levels and the frequency of LDL cholesterol measurements, a positive correlation was observed between the severity of sleep apnea and visit-to-visit LDL cholesterol variability. Our objective was to assess the effects of hypercholesterolemia on cardiac autonomic activity, disordered sleep patterns, and increased incidence of arrhythmias in freely moving rats...

Sleep stage classification is a necessary step for diagnosing sleep disorders. Generally, experts use traditional methods based on every 30 seconds (s) of the biological signals, such as electrooculograms (EOGs), electrocardiograms (ECGs), electromyograms (EMGs), and electroencephalograms (EEGs), to classify sleep stages. Recently, various state-of-the-art approaches based on a deep learning model have been demonstrated to have efficient and accurate outcomes in sleep stage classification. In this paper, a novel deep convolutional neural network (CNN) combined with a long short-time memory (LSTM) model is proposed for sleep scoring tasks...

PURPOSE: Persistent inward calcium and sodium currents (PICs) are crucial for initiation and maintenance of motoneuron firing, and thus muscular force. However, there is a lack of data describing the effects of fatiguing exercise on PIC activity in humans. We simultaneously applied tendon vibration and neuromuscular electrical stimulation (VibStim) before and after fatiguing exercise. VibStim induces self-sustained muscle activity that is proposed to result from PIC activation. METHODS: Twelve men performed 5-s maximal isometric plantar flexor contractions (MVC) with 5-s rests until joint torque was reduced to 70%MVC...

Needle electromyogram (EMG) research has suggested that endplate noise (EPN) is a characteristic of myofascial trigger points (MTrPs). Although several studies have observed MTrPs through ultrasonography, whether they are hyperechoic or hypoechoic in ultrasound images is still controversial. Therefore, this study determined the echogenicity of MTrP ultrasonography. In stage 1, the MTrP of rat masseter muscle was identified through palpation and marked. Needle EMG was performed to detect the presence of EPN...