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Can SEMG be used to detect changes in muscles in response to nerve compression caused be herniated discs?

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Surface electromyography (SEMG) can indeed be utilized to detect changes in muscles in response to nerve compression caused by herniated discs. Research has shown that SEMG parameters can aid in diagnosing compressed nerve roots at specific levels in patients with lumbar disc herniation, achieving high diagnostic accuracy . SEMG is a valuable tool for assessing muscle activation, which is crucial for diagnosing, treating, and monitoring conditions related to muscle function and rehabilitation . Additionally, innovative SEMG sensor systems have been developed to monitor muscular activation accurately, even when applied autonomously by patients, making it a promising technology for home and community settings . By analyzing SEMG signals and parameters, changes in muscle responses to nerve compression due to herniated discs can be effectively detected and diagnosed.

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Open access•Journal Article•DOI Introduction of a sEMG Sensor System for Autonomous Use by Inexperienced Users Elisa Romero Avila, Elmar Junker, Catherine Disselhorst-Klug - Show less +2 more 21 Dec 2020-Sensors 2 Citations	Not addressed in the paper.
Open access•Journal Article•DOI Diagnosis of Compressed Nerve Root in Lumbar Disc Herniation Patients by Surface Electromyography. Wei Li, Yancheng Liu, Chen‐fan Zheng, Jun Miao, Hui Chen, Hai‐ying Quan, Songhua Yan, Kuan Zhang - Show less +7 more 01 Feb 2018-Orthopaedic Surgery 12 Citations	Yes, SEMG can detect muscle changes due to nerve compression from herniated discs. The study established a logistic regression model with high accuracy for diagnosing compressed nerve roots in LDH patients.
Open access•Posted Content•DOI Overview of processing techniques for surface electromyography signals 08 Apr 2023	Not addressed in the paper.
Peer Review Overview of processing techniques for surface electromyography signals Edith Pulido-Herrera, John Jairo Villarejo Mayor - Show less +1 more 08 Apr 2023	Not addressed in the paper.
Open access•Journal Article•DOI SEMG Signals Identification Using DT And LR Classifier by Wavelet-Based Features Yogendra Narayan, Meet Kumari, Rajeev Ranjan - Show less +2 more 30 Dec 2022-International journal of electrical & electronics research 1 Citations	Not addressed in the paper.

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How does surface electromyography (sEMG) compare to other methods for assessing iliopsoas activity and function?4 answersSurface electromyography (sEMG) is a valuable technology for assessing muscle activation, including the iliopsoas, due to its non-invasive nature and ability to reflect neuromuscular activity. sEMG involves signal analysis techniques like digital processing and machine learning, making it a promising tool for identifying muscular disorders and abnormalities. Studies have shown that sEMG can accurately estimate muscle force in various tasks, including isometric voluntary flexions and standing activities, with high precision and efficiency compared to other methods like Artificial Neural Networks and Supported Vector Machines. Additionally, advancements in sEMG signal processing have led to the development of integrated hardware and software systems that automate signal analysis, providing key indicators of muscle activation intervals and amplitudes for easy interpretation.

How does the use of surface electromyography (SEMG) affect the performance of goalball players?4 answersSurface electromyography (sEMG) is a non-invasive method of measuring neuromuscular potentials generated during muscle contraction. It has been extensively studied and has shown potential in various applications such as clinical medicine, rehabilitation, and sports science. sEMG can be used to assess muscle activation and identify disorders or abnormalities through careful signal analysis. It has also been used as a sensing modality for natural human-machine interfaces, enabling high-bandwidth and seamless interactions between humans and machines. In the context of goalball players, the use of sEMG can potentially provide insights into muscle activation patterns and help in assessing performance. However, there is no specific mention of the impact of sEMG on goalball players in the provided abstracts.

What is the role of electromyogram (EMG) and nerve conduction studies (NCS) in diagnosing nerve and muscle disorders?4 answersElectromyogram (EMG) and nerve conduction studies (NCS) play a crucial role in diagnosing nerve and muscle disorders. EMG measures the electrical activity of muscles at rest and during use, providing valuable information about muscle function. NCS evaluates the function of motor and sensory nerves, assessing their ability to conduct electrical signals. These tests are used to diagnose and monitor conditions such as Hansen's disease, neuromuscular disorders in children, and myopathies. EMG and NCS can detect abnormalities in nerve and muscle function, helping to confirm diagnoses, identify the underlying cause, and guide further investigations such as muscle biopsy or genetic testing. They are particularly useful in distinguishing between different types of muscle diseases, such as myopathy and amyotrophic lateral sclerosis (ALS). Overall, EMG and NCS are essential tools in the diagnostic process for nerve and muscle disorders, providing valuable information for patient management and treatment decisions.

What is the amplitude of the EMG signal of spasm ?4 answersThe amplitude of the EMG signal during spasm varies depending on the condition. In patients with spasticity, the amplitude of the F response (F32) was found to be significantly increased. However, in another study, the amplitude of the EMG signal during spasm was not specifically mentioned. Therefore, it is important to consider the specific context and condition when determining the amplitude of the EMG signal during spasm.

How do linear and nonlinear sEMG methods compare in their ability to detect muscle fatigue during dynamic biceps curls?5 answersLinear and nonlinear sEMG methods have been compared in their ability to detect muscle fatigue during dynamic biceps curls. The study by Corvini and Conforto found that the autoregressive parametric approach outperformed the Welch technique in estimating mean and median frequency during severe muscle fatigue. Another study by Liao et al. used a nonlinear method called modified sample entropy (Ems) to assess muscle fatigue during dynamic contractions. They found that Ems was more sensitive to muscle fatigue and yielded more consistent results compared to traditional linear measures. Additionally, Hari et al. analyzed dynamic contractions and fatigue in the biceps brachii muscle using the synchrosqueezed wavelet transform (SST) and singular value features of sEMG signals. They found that the proposed approach was able to characterize nonstationary variations in sEMG signals during dynamic fatiguing contractions. Overall, these studies suggest that nonlinear methods may provide more accurate and sensitive detection of muscle fatigue during dynamic biceps curls compared to linear methods.

What effect does sensor area have on sEMG signals?5 answersThe effect of sensor area on sEMG signals has been explored in several papers. Jha and Sen proposed a simple and economic construction of a sEMG sensor and analyzed the relationship between electromyography and factors like age, gender, and health condition. Prakash, Kumari, and Sharma developed a low-cost and sensitive sEMG sensor for myoelectric prosthesis, which showed enhanced signal-to-noise ratio (SNR) and sensitivity compared to a commercial sensor. Avila, Junker, and Disselhorst-Klug demonstrated a proof-of-principle of an innovative sEMG sensor system that can be easily placed and removed by patients with reduced arm function, while maintaining comparable signal quality to commercial systems. Jiang, Sakoda, Togane, Morishita, and Yokoi proposed a novel sEMG sensor using polypyrrole-coated nonwoven fabric sheet as electrodes, which showed high correlation with traditional Ag/AgCl electrodes. Shen, Duan, Tan, and Wang presented a pattern recognition method for sEMG signals, achieving recognition rates of over 90% using three sensors placed on the forearm.

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