Neuromuscular recruitment pattern in motor point stimulation

Digital Biomarker for Muscle Function Assessment Using Surface Electromyography With Electrical Stimulation and a Non-Invasive Wearable Device

Sarcopenia is a comprehensive degenerative disease with the progressive loss of skeletal muscle mass with age, accompanied by the loss of muscle strength and muscle dysfunction. Individuals with unmanaged sarcopenia may experience adverse outcomes. Periodically monitoring muscle function to detect muscle degeneration caused by sarcopenia and treating degenerated muscles is essential. We proposed a digital biomarker measurement technique using surface electromyography (sEMG) with electrical stimulation and wearable device to conveniently monitor muscle function at home. When motor neurons and muscle fibers are electrically stimulated, stimulated muscle contraction signals (SMCSs) can be obtained using an sEMG sensor. As motor neuron activation is important for muscle contraction and strength, their action potentials for electrical stimulation represent the muscle function. Thus, the SMCSs are closely related to muscle function, presumptively. Using the SMCSs data, a feature vector concatenating spectrogram-based features and deep learning features extracted from a convolutional neural network model using continuous wavelet transform images was used as the input to train a regression model for measuring the digital biomarker. To verify muscle function measurement technique, we recruited 98 healthy participants aged 20-60 years including 48 [49%] men who volunteered for this study. The Pearson correlation coefficient between the label and model estimates was 0.89, suggesting that the proposed model can robustly estimate the label using SMCSs, with mean error and standard deviation of -0.06 and 0.68, respectively. In conclusion, measuring muscle function using the proposed system that involves SMCSs is feasible.

Sarcopenia Diagnostic Technique Based on Artificial Intelligence Using Bio-signal of Neuromuscular System: A Proof-of-Concept Study

In this paper, we propose an artificial intelligence (AI)-based sarcopenia diagnostic technique for stroke patients utilizing bio-signals from the neuromuscular system. Handgrip, skeletal muscle mass index, and gait speed are prerequisite components for sarcopenia diagnoses. However, measurement of these parameters is often challenging for most hemiplegic stroke patients. For these reasons, there is an imperative need to develop a sarcopenia diagnostic technique that requires minimal volitional participation but nevertheless still assesses the muscle changes related to sarcopenia. The proposed AI diagnostic technique collects motor unit responses from stroke patients in a resting state via stimulated muscle contraction signals (SMCSs) recorded from surface electromyography while applying electrical stimulation to the muscle. For this study, we extracted features from SMCS collected from stroke patients and trained our AI model for sarcopenia diagnosis. We validated the performance of the trained AI models for each gender against other diagnostic parameters. The accuracy of the AI sarcopenia model was 96%, and 95% for male and females, respectively. Through these results, we were able to provide preliminary proof that SMCS could be a potential surrogate biomarker to reflect sarcopenia in stroke patients.

Spinal Orthosis in Adolescent Idiopathic Scoliosis: An Overview of the Braces Provided by the National Health Service in Italy

Adolescent idiopathic scoliosis (AIS) is a lateral, rotated curvature of the spine. It is a 3-dimensional deformity that arises in otherwise healthy children at or around puberty. AIS is the most common form of scoliosis in the pediatric population. The etiology is multifactorial, including genetic and environmental factors. The incidence is roughly equal between males and females, while there is a higher risk of progression in females. Guidelines for AIS treatment identify three levels of treatment: observation, physiotherapy scoliosis-specific exercises, and braces. In this paper, we carried out a review of the scientific literature about the indication and success rates of the braces provided for free by the National Health Service in Italy (SSN). Despite a general consensus on the efficacy of rigid bracing treatment and its use in AIS, an important heterogeneity about the treatment is present in the scientific literature, demonstrating a high degree of variability. The overall success rate of the braces provided by the SSN is high, suggesting an important therapeutic role in the treatment of AIS. Robust guidelines are needed to ensure uniform and effective treatments.

Methods for automated delineation and assessment of EMG responses evoked by peripheral nerve stimulation in diagnostic and closed-loop therapeutic applications

Objective.Surface electromyography measurements of the Hoffmann (H-) reflex are essential in a wide range of neuroscientific and clinical applications. One promising emerging therapeutic application is H-reflex operant conditioning, whereby a person is trained to modulate the H-reflex, with generalized beneficial effects on sensorimotor function in chronic neuromuscular disorders. Both traditional diagnostic and novel realtime therapeutic applications rely on accurate definitions of the H-reflex and M-wave temporal bounds, which currently depend on expert case-by-case judgment. The current study automates such judgments.Approach.Our novel wavelet-based algorithm automatically determines temporal extent and amplitude of the human soleus H-reflex and M-wave. In each of 20 participants, the algorithm was trained on data from a preliminary 3 or 4 min recruitment-curve measurement. Output was evaluated on parametric fits to subsequent sessions' recruitment curves (92 curves across all participants) and on the conditioning protocol's subsequent baseline trials (∼1200 per participant) performed nearHmax. Results were compared against the original temporal bounds estimated at the time, and against retrospective estimates made by an expert 6 years later.Main results.Automatic bounds agreed well with manual estimates: 95% lay within ±2.5 ms. The resulting H-reflex magnitude estimates showed excellent agreement (97.5% average across participants) between automatic and retrospective bounds regarding which trials would be considered successful for operant conditioning. Recruitment-curve parameters also agreed well between automatic and manual methods: 95% of the automatic estimates of the current required to elicitHmaxfell within±1.4%of the retrospective estimate; for the 'threshold' current that produced an M-wave 10% of maximum, this value was±3.5%.Significance.Such dependable automation of M-wave and H-reflex definition should make both established and emerging H-reflex protocols considerably less vulnerable to inter-personnel variability and human error, increasing translational potential.