Diagnosis Of Muscle Disease Research Articles

Muscle fibre morphometric analysis (MusMA) correlates with muscle function and cardiovascular risk prognosis.

Morphometry of striated muscle fibres is critical for monitoring muscle health and function. Here, we evaluated functional parameters of skeletal and cardiac striated muscle in two experimental models using the Morphometric Analysis of Muscle Fibre tool (MusMA). The collagen-induced arthritis model was used to evaluate the function of skeletal striated muscle and the non-alcoholic fatty liver disease model was used for cardiac striated muscle analysis. After euthanasia, we used haeamatoxylin and eosin stained sections of skeletal and cardiac muscle to perform muscle fibre segmentation and morphometric analysis. Morphometric analysis classified muscle fibres into six subpopulations: normal, regular hypertrophic, irregular hypertrophic, irregular, irregular atrophic and regular atrophic. The percentage of atrophic fibres was associated with lower walking speed (p = 0.009) and lower body weight (p = 0.026), respectively. Fibres categorized as normal were associated with maximum grip strength (p < 0.001) and higher march speed (p < 0.001). In the evaluation of cardiac striated muscle fibres, the percentage of normal cardiomyocytes negatively correlated with cardiovascular risk markers such as the presence of abdominal adipose tissue (p = .003), miR-33a expression (p = .001) and the expression of miR-126 (p = .042) Furthermore, the percentage of atrophic cardiomyocytes correlated significantly with the Castelli risk index II (p = .014). MusMA is a simple and objective tool that allows the screening of striated muscle fibre morphometry, which can complement the diagnosis of muscle diseases while providing functional and prognostic information in basic and clinical research.

Current and Future Status of Muscle Pathology: The Position of Muscle Pathology Diagnosis in the Future

Many muscle disease names are mostly based on muscle pathology findings. Naturally, muscle pathology is important in the diagnosis of muscle diseases. Moreover, in recent years, extensive genetic analysis and autoantibody testing for myositis have been applied clinically, although muscle biopsies are less performed. However, muscle pathology should be proactively considered when a single gene presents multiple phenotypes, when variants of unknown pathological significance are detected, or in cases of autoimmune myositis that may be misdiagnosed as muscular dystrophy.

MyoV: a deep learning-based tool for the automated quantification of muscle fibers.

Accurate approaches for quantifying muscle fibers are essential in biomedical research and meat production. In this study, we address the limitations of existing approaches for hematoxylin and eosin-stained muscle fibers by manually and semiautomatically labeling over 660000 muscle fibers to create a large dataset. Subsequently, an automated image segmentation and quantification tool named MyoV is designed using mask regions with convolutional neural networks and a residual network and feature pyramid network as the backbone network. This design enables the tool to allow muscle fiber processing with different sizes and ages. MyoV, which achieves impressive detection rates of 0.93-0.96 and precision levels of 0.91-0.97, exhibits a superior performance in quantification, surpassing both manual methods and commonly employed algorithms and software, particularly for whole slide images (WSIs). Moreover, MyoV is proven as a powerful and suitable tool for various species with different muscle development, including mice, which are a crucial model for muscle disease diagnosis, and agricultural animals, which are a significant meat source for humans. Finally, we integrate this tool into visualization software with functions, such as segmentation, area determination and automatic labeling, allowing seamless processing for over 400000 muscle fibers within a WSI, eliminating the model adjustment and providing researchers with an easy-to-use visual interface to browse functional options and realize muscle fiber quantification from WSIs.

Estimation of Muscle Fiber Direction by Multi-channel Surface EMG Conductiong Wave Analysis Using Grid Surface Electrodes

Skeletal muscle is a set of motor units (MU). Muscle contractile activity is carried out by regulating the firing frequency of MU and the types of muscle fibers to be mobilized. Multi-channel surface electromyogram (sEMG) contains many conducting waves that represent a single motor unit action potentials (MUAP). In previous study, we proposed a method to extract conducting waves quantitatively and automatically. This method is useful for elucidating mobilized MUs and can be applied to kinematic analysis and diagnosis of muscle diseases. In multi-channel sEMG measurement, rows of electrodes need to be applied along the direction of the muscle fibers. However, the electrodes are difficult to set because the direction of the muscle fibers cannot be visually confirmed on the skin. This study investigated a method for estimating muscle fiber direction by analyzing conducting waves from two-dimensional multichannel surface electromyograms using grid-shaped surface electrodes. By examining the number of conducting waves acquired in each direction of the electrode row, it was suggested that the direction of muscle fiber may be estimated from the direction with the largest number of propagating wave acquisitions. It was also found that the method of acquiring differential potential signals has a significant impact on the analysis of propagation direction.

Passive constitutive theory of a single muscle fiber for the potential diagnosis of muscle diseases at the molecular level

Titins are mainly responsible for passive mechanical properties of a single muscle fiber, which, however, can be compromised by the change in molecular structures of titins occurring in some muscle-related diseases. Based on molecular structures of titins within a sarcomere unit, we have developed a constitutive theory for the passive elasticity of a single muscle fiber with the utilization of the virtual work principal. The number density of titins, domain composition of titins, the kinetics of folded domains within titins, the interaction between the water and molecular chains, the sarcomere length, etc., can all be considered in the theory. The predicted stress-strain curves based on the theory are in agreement with experiment. We also demonstrate that abnormal structural parameters of titins from patients with chronic obstructive pulmonary disease or muscle atrophy caused by long-term disuse can be extracted with the theory. We then suggest that this constitutive theory may potentially facilitate the molecular diagnosis of muscle diseases exhibiting abnormality in the passive elasticity .

N-CAM Expression: The Study of Muscle Disease in a Tertiary Center of Thailand

Background: Neural cell adhesion molecule (N-CAM), or CD56, is a cell-surface glycoprotein that plays a critical role in mediating intercellular adhesion in the central nervous system. It is also found to be expressed on embryonic muscle but disappears in healthy adult muscle. However, denervated or regenerating muscles can express N-CAM. Objective: To evaluate the value of N-CAM expressions in diagnosing muscle diseases in the Thai population. Materials and Methods: The present study used immunohistochemistry to interpret N-CAM expression in 75 muscle biopsy specimens diagnosed with myopathies and non-myopathies from a 3-year retrospective cohort. Results: Using the chi-squared test, there was statistically significant associativity between N-CAM expression and the diagnosis of inflammatory myopathy, but not in muscular dystrophy, neurogenic change, or non-specific myopathy even though they also showed N-CAM expression. The inflammatory myopathy showed positive N-CAM in 15 out of 17 cases with statistical significance (p&lt;0.001, OR 14.250, 95% CI 2.960 to 68.606). The authors did not find any N-CAM expression in the present study mitochondrial myopathy or non-myopathy cases, whose biopsies were defined as a control group due to lack of myopathic change and non-specific clinical symptoms. Conclusion: The authors suggest the evaluation of N-CAM expression as a complementary tool in the diagnosis of muscle diseases that have denervated or regenerating muscle fibers, especially in inflammatory myopathy. Keywords: Neural cell adhesion molecule; N-CAM; CD56; Muscle disease; Myopathy

Fully Automatic Analysis of Muscle B-Mode Ultrasound Images Based on the Deep Residual Shrinkage U-Net

The parameters of muscle ultrasound images reflect the function and state of muscles. They are of great significance to the diagnosis of muscle diseases. Because manual labeling is time-consuming and laborious, the automatic labeling of muscle ultrasound image parameters has become a research topic. In recent years, there have been many methods that apply image processing and deep learning to automatically analyze muscle ultrasound images. However, these methods have limitations, such as being non-automatic, not applicable to images with complex noise, and only being able to measure a single parameter. This paper proposes a fully automatic muscle ultrasound image analysis method based on image segmentation to solve these problems. This method is based on the Deep Residual Shrinkage U-Net(RS-Unet) to accurately segment ultrasound images. Compared with the existing methods, the accuracy of our method shows a great improvement. The mean differences of pennation angle, fascicle length and muscle thickness are about 0.09°, 0.4 mm and 0.63 mm, respectively. Experimental results show that the proposed method realizes the accurate measurement of muscle parameters and exhibits stability and robustness.

Analysis of muscle tissue in vivo using fiber-optic autofluorescence and diffuse reflectance spectroscopy.

.Significance: Current methods for analyzing pathological muscle tissue are time consuming and rarely quantitative, and they involve invasive biopsies. Faster and less invasive diagnosis of muscle disease may be achievable using marker-free in vivo optical sensing methods.Aim: It was speculated that changes in the biochemical composition and structure of muscle associated with pathology could be measured quantitatively using visible wavelength optical spectroscopy techniques enabling automated classification.Approach: A fiber-optic autofluorescence (AF) and diffuse reflectance (DR) spectroscopy device was manufactured. The device and data processing techniques based on principal component analysis were validated using in situ measurements on healthy skeletal and cardiac muscle. These methods were then applied to two mouse models of genetic muscle disease: a type 1 neurofibromatosis (NF1) limb-mesenchyme knockout () and a muscular dystrophy mouse (mdx).Results: Healthy skeletal and cardiac muscle specimens were separable using AF and DR with receiver operator curve areas (ROC-AUC) of . AF and DR analyses showed optically separable changes in quadriceps muscle (ROC-AUC >0.97) with no differences detected in the heart (ROC-AUC <0.67), which does not undergo gene deletion in this model. Changes in AF spectra in mdx muscle were seen between the 3 week and 10 week time points (ROC-AUC = 0.96) and were not seen in the wild-type controls (ROC-AUC = 0.58).Conclusion: These findings support the utility of in vivo fiber-optic AF and DR spectroscopy for the assessment of muscle tissue. This report highlights that there is considerable scope to develop this marker-free optical technology for preclinical muscle research and for diagnostic assessment of clinical myopathies and dystrophies.

Estimation of Knee Joint Extension Force Using Mechanomyography Based on IGWO-SVR Algorithm

Muscle force is an important physiological parameter of the human body. Accurate estimation of the muscle force can improve the stability and flexibility of lower limb joint auxiliary equipment. Nevertheless, the existing force estimation methods can neither satisfy the accuracy requirement nor ensure the validity of estimation results. It is a very challenging task that needs to be solved. Among many optimization algorithms, gray wolf optimization (GWO) is widely used to find the optimal parameters of the regression model because of its superior optimization ability. Due to the traditional GWO being prone to fall into local optimum, a new nonlinear convergence factor and a new position update strategy are employed to balance local and global search capability. In this paper, an improved gray wolf optimization (IGWO) algorithm to optimize the support vector regression (SVR) is developed to estimate knee joint extension force accurately and timely. Firstly, mechanomyography (MMG) of the lower limb is measured by acceleration sensors during leg isometric muscle contractions extension training. Secondly, root mean square (RMS), mean absolute value (MAV), zero crossing (ZC), mean power frequency (MPF), and sample entropy (SE) of the MMG are extracted to construct feature sets as candidate data sets for regression analysis. Lastly, the features are fed into IGWO-SVR for further training. Experiments demonstrate that the IGWO-SVR provides the best performance indexes in the estimation of knee joint extension force in terms of RMSE, MAPE, and R compared with the other state-of-art models. These results are expected to become the most effective as guidance for rehabilitation training, muscle disease diagnosis, and health evaluation.

Automated diagnosis of muscle diseases from EMG signals using empirical mode decomposition based method

Muscle activity decreases due to various conditions like age factors and muscle diseases namely, amyotrophic lateral sclerosis (ALS) and myopathy. Electromyogram (EMG) signals are regularly explored by specialists to analyze the irregularity of muscles. Manual investigation of EMG signals is a tedious task for medical practitioners. Therefore, this work proposes a new method for classifying the ALS, myopathy, and normal EMG signals. In the proposed method, the empirical mode decomposition (EMD) method is applied to decompose the EMG signals into intrinsic mode functions (IMFs). The suitable IMFs for feature selection are selected using the t-test based approach and used to compute the foot distances denoted as fp1 and fp2 by constructing the complex plane plot. The quadrilateral is drawn over a complex plot by considering fp1 and fp2 as a diagonal of it, followed by calculating the area (A) and circumference (CF) of the quadrilateral. These measures are utilized for separating the three classes of myopathy, ALS, and normal EMG signals. The proposed algorithm has been trained and validated using a feed forward neural network (FFNN), support vector machine (SVM), and decision tree. The algorithm, when tested with a FFNN, achieved the maximum classification accuracy, sensitivity, and specificity of 99.53%, 99.25% and 99.60%, respectively.

Diagnosis of Muscle Diseases at the Molecular Level With a Passive Constitutive Theory for a Single Muscle Fiber

Titins are mainly responsible for passive mechanical properties of single muscle fibers, which, however, can be compromised by the change in molecular structures of titins occurring in some muscle-related diseases. Based on molecular structures of titins within a sarcomere unit, we have developed a constitutive theory for the passive elasticity of single muscle fibers. The theory prediction is in agreement with experiment. With the utilization of the theory, we demonstrate that abnormal structural parameters of titins from patients with chronic obstructive pulmonary disease or muscle atrophy caused by long-term disuse can be extracted. We then suggest that this constitutive theory may facilitate the molecular diagnosis of muscle diseases exhibiting abnormality in the passive elasticity.

Muscle pathology in neuromuscular disorders

Muscle pathology findings may guide the diagnosis of neuromuscular disorders since they are helpful for understanding the pathological processes causing muscle weakness and also provide significant clues for the diagnosis of muscle diseases. Recent advances in molecular genetics mean that a muscle biopsy can be omitted when diagnosing inherited muscle diseases. However, the muscle pathology can still play a role in those cases and its findings are also required when diagnosing inflammatory myopathies. Key Words: Muscle weakness · Pathology · Muscular dystrophies · Congenital myopathies · Inflammatory myopathy.

OMICs AND AI APPROACHES FOR MUSCLE DISEASES: O.08 Deep convolutional-neural-network can differentiate twelve major muscular diseases better than human

Muscle pathology plays an important role in the diagnosis of muscle disease even in the molecular era, as most muscle diseases have been classified or even partly defined by pathological features. Nevertheless, availability of experts is limited especially in underserved areas of the world. This study aims to establish the algorithm to assist muscle pathology diagnosis using deep convolutional neural networks (CNNs), which can differentiate major muscle diseases only by hematoxylin and eosin (H&E)-stained pathological images. Target diseases were dystrophinopathy (DYST), limb-girdle muscular dystrophy (LGMD) 2A (LG2A), LGMD2B (LG2B), Ullrich congenital muscular dystrophy (CMD) (UCMD), Fukuyama CMD (FCMD), congenital myopathy (CM), GNE myopathy (GNEM), dermatomyositis (DM), inclusion body myositis (IBM), immune-mediated necrotizing myopathy (IMNM), antisynthetase syndrome (ASS), and neuropathic disease (NP). All images to train CNNs were taken from slides with CCD cameras via light microscopes. A total of 4041 images were obtained from 1400 slides. The algorithm consists of two steps: 1) to differentiate between idiopathic inflammatory myopathy (IIM) and other conditions, and 2) to subclassify each category. For the first step, images of DM, IBM, IMNM, and ASS were combined to create the IIM group. Meanwhile, those of DYST, FCMD, LG2A, LG2B, UCMD, CM, GNEM, and NP were combined to create another group. At the second step, four subtypes of IIM were analyzed. The first step achieved 0.996 area under the curve (AUC). The accuracy of the CNN and the best pathologist of all were 96.9% and 93.8%, respectively, indicating the CNN outperformed muscle specialists. In the second step, the CNNs classified four subtypes (DM, IBM, IMNM, and ASS) with high AUCs: 0.953 (IMNM), 0.969 (IBM), 0.965 (DM), and 0.944 (ASS). The algorithm used only a few H&E images obtained by CCD, which does not need a huge financial investment. This system should be helpful in clinical settings especially in underserved regions, where experts are not easily available.

Role of Ultrasound in Diagnosis of Muscle Diseases

Role of Ultrasound in Diagnosis of Muscle Diseases

Diagnosing Muscle Atrophy by Use of a Comprehensive Method of Assessing the Elastic Properties of Muscle During Passive Stretching.

OBJECTIVE. The purpose of this study was to apply a new method for quantitatively assessing atrophied muscles by use of sonoelastography. SUBJECTS AND METHODS. B-mode sonography and shear-wave elastography of the gastrocnemius muscle (GM) were performed on 12 patients and 32 healthy control volunteers during passive stretching induced by ankle rotation from 40° plantar flexion to 30° dorsiflexion. The GM length and corresponding elastic moduli were measured consecutively per frame. The elastic modulus-length curve was created by plotting the elastic modulus against the GM length during passive stretching. Three physiologically significant parameters-slack elasticity modulus (E0), slack length (l0), and passive elastic coefficient (k)-were estimated from the measured elasticity modulus-length curve to quantify the elastic properties of the GM. RESULTS. All results showed a smooth exponential curve. In the state of relaxation, atrophied GM was softer than GM of healthy control subjects. However, when the muscles were passively stretched from a state of slack to tension, k was significantly greater for the patients with atrophied GMs than for the control subjects. Furthermore, the diagnostic accuracy of k for muscle atrophy was greater than that of E0. CONCLUSION. Increased elastic stiffness may be considered a positive finding for the characterization of muscle atrophy. Because of its high diagnostic accuracy and reproducibility, the elastic modulus-length curve can provide new insights into the diagnosis of muscle disease and allows monitoring of muscle function in rehabilitation.

Strength training and aerobic exercise training for muscle disease.

Background Strength training or aerobic exercise programmes might optimise muscle and cardiorespiratory function and prevent additional disuse atrophy and deconditioning in people with a muscle disease. This is an update of a review first published in 2004. Objectives To examine the safety and efficacy of strength training and aerobic exercise training in people with a muscle disease. Search methods We searched the Cochrane Neuromuscular Disease Group Specialized Register (July 2012), CENTRAL (2012 Issue 3 of 4), MEDLINE (January 1946 to July 2012), EMBASE (January 1974 to July 2012), EMBASE Classic (1947 to 1973) and CINAHL (January 1982 to July 2012). Selection criteria Randomised or quasi-randomised controlled trials comparing strength training or aerobic exercise programmes, or both, to no training, and lasting at least six weeks, in people with a well-described diagnosis of a muscle disease. We did not use the reporting of specific outcomes as a study selection criterion. Data collection and analysis Two authors independently assessed trial quality and extracted the data obtained from the full text-articles and from the original investigators. We collected adverse event data from included studies. Main results We included five trials (170 participants). The first trial compared the effect of strength training versus no training in 36 people with myotonic dystrophy. The second trial compared aerobic exercise training versus no training in 14 people with polymyositis and dermatomyositis. The third trial compared strength training versus no training in a factorial trial that also compared albuterol with placebo, in 65 people with facioscapulohumeral muscular dystrophy (FSHD). The fourth trial compared combined strength training and aerobic exercise versus no training in 18 people with mitochondrial myopathy. The fifth trial compared combined strength training and aerobic exercise versus no training in 35 people with myotonic dystrophy type 1. In both myotonic dystrophy trials and the dermatomyositis and polymyositis trial there were no significant differences between training and non-training groups for primary and secondary outcome measures. The risk of bias of the strength training trial in myotonic dystrophy and the aerobic exercise trial in polymyositis and dermatomyositis was judged as uncertain, and for the combined strength training and aerobic exercise trial, the risk of bias was judged as adequate. In the FSHD trial, for which the risk of bias was judged as adequate, a +1.17 kg difference (95% confidence interval (CI) 0.18 to 2.16) in dynamic strength of elbow flexors in favour of the training group reached statistical significance. In the mitochondrial myopathy trial, there were no significant differences in dynamic strength measures between training and non-training groups. Exercise duration and distance cycled in a submaximal endurance test increased significantly in the training group compared to the control group. The differences in mean time and mean distance cycled till exhaustion between groups were 23.70 min (95% CI 2.63 to 44.77) and 9.70 km (95% CI 1.51 to 17.89), respectively. The risk of bias was judged as uncertain. In all trials, no adverse events were reported. Authors' conclusions Moderate-intensity strength training in myotonic dystrophy and FSHD and aerobic exercise training in dermatomyositis and polymyositis and myotonic dystrophy type I appear to do no harm, but there is insufficient evidence to conclude that they offer benefit. In mitochondrial myopathy, aerobic exercise combined with strength training appears to be safe and may be effective in increasing submaximal endurance capacity. Limitations in the design of studies in other muscle diseases prevent more general conclusions in these disorders.

A Novel HD-sEMG Preprocessing Method Integrating Muscle Activation Heterogeneity Analysis and Kurtosis-Guided Filtering for High-Accuracy Joint Force Estimation.

This study proposes a novel preprocessing method integrating muscle activation heterogeneity analysis and kurtosis-guided filtering to realize high-accuracy surface electromyogr-aphy (sEMG)-based force estimation. A total of 10 subjects were recruited. Each subject performed isometric elbow flexion tasks at 20%, 40%, and 60% maximum voluntary contraction (MVC) target force levels, and the joint force and high-density sEMG (HD-sEMG) signals from biceps brachii and brachialis were collected synchronously. The force estimation model was built using three-order polynomial fitting technique. The input signal extraction of the force model, also named as the preprocessing of HD-sEMG signal, was carried out in the following procedures: first, HD-sEMG signals were decomposed by principal component analysis into principal components and weight vectors; second, the first several weight maps were segmented to obtain heterogeneity information by the Otsu and Moore-Neighbor tracing methods, and the principal component covering the most activated areas was selected; and last, a kurtosis-guided filter was designed to process the selected principal component to obtain the input signal. For the sake of comparison, the joint force estimation experiments based ON five preprocessing methods were conducted. The experimental results demonstrated that the proposed method obtained 52%, 53%, and 59% reduction in the mean root mean square difference at 20% MVC, 40% MVC, and 60% MVC force-level tasks, respectively, compared to the preprocessing method with the first principal component plus fixed parameter filtering. This proposed HD-sEMG pre-processing method has reliable neuromuscular electro-physiological foundation, and has good application value for realizing high-accuracy muscle/joint force estimation in the fields of rehabilitation engineering, sports biomechanics, and muscle disease diagnosis etc.

The role of muscle ultrasound in helping the clinical diagnosis of muscle diseases

BackgroundSelective involvement of certain muscles is an indicator for muscle diseases and helps to direct the diagnosis, but in some cases, it cannot be detected clinically; hence, the roles of muscle MRI and ultrasound are to detect this selectivity and facilitate the diagnosis.ObjectivesThe possibility of using muscle ultrasound as a screening tool when muscle diseases are suspected and as an alternative to MRI.Subjects and methodsThis cross-sectional descriptive study included 38 patients presented with clinical manifestations suggestive of muscle diseases. The patients were selected over a period of 1 year. All patients were subjected to thorough clinical assessment and muscle ultrasound of the thigh and leg for all patients, while 15 were subjected to MRI. Clinical and radiological assessments were performed separately, followed by both clinical and radiological findings to assess the power of combining the clinical and radiological assessments for the diagnosis of muscle diseases.ResultsThe clinical assessment reached a main provisional probable diagnosis in 53% cases, and radiological assessment blind to clinical data suggested diagnosis in 18 of the total cases, while the combination of both ultrasound and MRI could suggest diagnosis in 87% of the cases. The concordance ratio of ultrasound to MRI ranged between 78 and 100%.ConclusionThe combination of clinical and radiological assessments of muscle diseases can suggest a main provisional probable diagnosis, especially when genetic diagnosis is not accessible, or to direct the genetic testing when it is available. Ultrasound can be used as a routine tool in screening and follow-up of muscle diseases.

Incidental hypertransaminasemia in children: Potential delay in diagnosis of muscle disease

Many physicians believe that muscle disease leads either to sole elevation of aspartate aminotransferase (AST) or a disproportionate increase in AST versus alanine aminotreansferase (ALT). We herein report 25 patients with muscle disease who presented erroneously to the hepatologist with an incidental finding of elevated ALT/AST. Data of 25 patients with elevated ALT/AST who proved to have muscle disease and underwent extensive liver workup were analyzed. The time lapse until diagnosis of muscle disease and costs of unnecessary liver workup were calculated. The time interval from the incidental finding of elevated transaminases to diagnosis of skeletal muscle disease ranged between 2 months to 10 years with a median (IQR) of 2 (3.6) years. The mean ALT:AST ratio was 1.24. Creatine phosphokinase (CPK) level correlated positively and significantly with both ALT (p = 0.002) and AST (p = 0.0007). The costs of unnecessary liver workup, before muscle disease was diagnosed, ranged between 5000 and 10,000 Egyptian pounds. Unawareness of physicians with muscle origin of ALT/AST may cause delay in diagnosis of muscle disease. Early determination of CPK could save a lot of stress and money.

HD-sEMG-based research on activation heterogeneity of skeletal muscles and the joint force estimation during elbow flexion

Objective. To investigate the activation heterogeneity of skeletal muscles and realize the joint force estimation during the elbow flexion task. Approach. When an isometric elbow flexion task was performed, high-density surface electromyography (HD-sEMG) signals from a grid covering the front and inside of the upper arm and the generated joint force were recorded synchronously. HD-sEMG signals were preprocessed and then decomposed into source signals corresponding to biceps brachhi (BB) and brachialis (BR) and their contribution vectors using a fast, independent component analysis (FastICA) algorithm. The activation heterogeneity of BB and BR was investigated from the activation level and activation region, initially. Then, the contribution combinations of two sources were classified into several major clusters using the K-means clustering method. Afterwards, input signals for force estimation were extracted from the major clusters corresponding to different combinations, and the polynomial fitting technique was adopted as the force estimation model. Finally, the force estimation results were obtained and the analysis around the force estimation performance using different input signals was conducted. Main results. Ten subjects were recruited in this research. The experimental results demonstrated that it is feasible to analyze the activation heterogeneity of muscles from the activation level and activation region, and to select the appropriate region of the HD-sEMG grid for high performance force estimation. For the isometric elbow flexion task, joint force estimation accuracy could be improved when the input signal was extracted from the specific area where the contribution difference of BB and BR to the HD-sEMG signals were relatively small. Significance. The proposed framework provided a novel way to explore the relationship between muscle activation and the generating joint force, and could be extended to multiple noteworthy research fields such as myoelectric prostheses, sports biomechanics, and muscle disease diagnosis.