Analysis of induced dynamic biceps EMG signal complexity using Markov transition networks.

Purpose: Surface electromyography (sEMG) is a non-invasive technique to characterize muscle electrical activity. The analysis of sEMG signals under muscle fatigue play a crucial part in the branch of neurorehabilitation, sports medicine, biomechanics, and monitoring neuromuscular pathologies. In this work, a method to transform sEMG signals to complex networks under muscle fatigue conditions using Markov transition field (MTF) is proposed. The importance of normalization to a constant Maximum voluntary contraction (MVC) is also considered. Methods: For this, dynamic signals are recorded using two different experimental protocols one under constant load and another referenced to 50% MVC from Biceps brachii of 50 and 45 healthy subjects respectively. MTF is generated and network graph is constructed from preprocesses signals. Features such as average self-transition probability, average clustering coefficient and modularity are extracted. Results: All the extracted features showed statistical significance for the recorded signals. It is found that during the transition from non-fatigue to fatigue, average clustering coefficient decreases while average self-transition probability and modularity increases. Conclusion: The results indicate higher degree of signal complexity during non-fatigue condition. Thus, the MTF approach may be used to indicate the complexity of sEMG signals. Although both datasets showed same trend in results, sEMG signals under 50% MVC exhibited higher separability for the features. The inter individual variations of the MTF features is found to be more for the signals recorded using constant load. The proposed study can be adopted to study the complex nature of muscles under various neuromuscular conditions.

Generation of synthetic surface electromyography signals under fatigue conditions for varying force inputs using feedback control algorithm.

Surface electromyography is a non-invasive technique used for recording the electrical activity of neuromuscular systems. These signals are random, complex and multi-component. There are several techniques to extract information about the force exerted by muscles during any activity. This work attempts to generate surface electromyography signals for various magnitudes of force under isometric non-fatigue and fatigue conditions using a feedback model. The model is based on existing current distribution, volume conductor relations, the feedback control algorithm for rate coding and generation of firing pattern. The result shows that synthetic surface electromyography signals are highly complex in both non-fatigue and fatigue conditions. Furthermore, surface electromyography signals have higher amplitude and lower frequency under fatigue condition. This model can be used to study the influence of various signal parameters under fatigue and non-fatigue conditions.

Analysis of Muscle Fatigue Progression using Cyclostationary Property of Surface Electromyography Signals.

Analysis of neuromuscular fatigue finds various applications ranging from clinical studies to biomechanics. Surface electromyography (sEMG) signals are widely used for these studies due to its non-invasiveness. During cyclic dynamic contractions, these signals are nonstationary and cyclostationary. In recent years, several nonstationary methods have been employed for the muscle fatigue analysis. However, cyclostationary based approach is not well established for the assessment of muscle fatigue. In this work, cyclostationarity associated with the biceps brachii muscle fatigue progression is analyzed using sEMG signals and Spectral Correlation Density (SCD) functions. Signals are recorded from fifty healthy adult volunteers during dynamic contractions under a prescribed protocol. These signals are preprocessed and are divided into three segments, namely, non-fatigue, first muscle discomfort and fatigue zones. Then SCD is estimated using fast Fourier transform accumulation method. Further, Cyclic Frequency Spectral Density (CFSD) is calculated from the SCD spectrum. Two features, namely, cyclic frequency spectral area (CFSA) and cyclic frequency spectral entropy (CFSE) are proposed to study the progression of muscle fatigue. Additionally, degree of cyclostationarity (DCS) is computed to quantify the amount of cyclostationarity present in the signals. Results show that there is a progressive increase in cyclostationary during the progression of muscle fatigue. CFSA shows an increasing trend in muscle fatiguing contraction. However, CFSE shows a decreasing trend. It is observed that when the muscle progresses from non-fatigue to fatigue condition, the mean DCS of fifty subjects increases from 0.016 to 0.99. All the extracted features found to be distinct and statistically significant in the three zones of muscle contraction (p < 0.05). It appears that these SCD features could be useful in the automated analysis of sEMG signals for different neuromuscular conditions.

Differentiating Muscle Fatigue and Nonfatigue Conditions Using Surface EMG Signals and Zhao-Atlas-Marks Based Time-Frequency Distribution.

Muscle fatigue is a neuromuscular condition where muscles fail to generate the required force. It occurs in normal as well as abnormal subjects. The analysis of muscle fatigue plays a significant role in the field of clinical studies, myo-electric control, ergonomics and sports biomechanics. In this work, an attempt has been made to differentiate the sEMG signals under muscle non-fatigue and fatigue conditions using Zhao-Atlas-Marks (ZAM) based time frequency distribution. For this purpose, sEMG signals are recorded from fifty healthy volunteers during isometric contractions under well defined protocol. The acquired signals are preprocessed and subjected to ZAM based time-frequency analysis. The time-frequency based features such as instantaneous median frequency (IMDF) and instantaneous mean frequency (IMNF) are extracted from the time-frequency spectrum. The results show that IMDF and IMNF are distinct for muscle non-fatigue and fatigue conditions. Further, more number of frequency components are observed in the time-frequency spectrum of signals recorded in nonfatigue conditions. The t-test performed on these features has shown significant difference (p<0.01) in between non-fatigue and fatigue conditions. Thus the study seems to be useful for the analysis of various neuromuscular conditions.

Analysis of Surface EMG Signals in Isometric Contraction at Different Angles Using Rainflow Counting Algorithms.

Endurance and muscle strengthening are required in a number of applications including fitness training, sports and prosthetics. Optimal performance can be achieved by understanding the relationship between muscle activity and angular variation of the elbow joint. Biceps brachii is an important muscle of the upper arm that helps in providing stability during flexion and extension. In this work, an attempt is made to analyze isometric contraction at various elbow angles using surface electromyography and rainflow counting algorithm. The signals are recorded from biceps brachii muscles of twenty healthy subjects while performing well defined protocol using a 6kg dumbbell at three different angles: 30°, 60° and 90°. The recorded signals are preprocessed and subjected to rainflow counting algorithm to obtain rainflow cycles. A new feature, cycle crossing intensity (CCI) is calculated from the rainflow cycles. The intensity peak parameter is computed from each CCI. The results show that the CCIs are distinct for all the three angles of elbow flexion and shifts towards higher magnitude values with increase in the angle of flexion. The average peak intensity is 425, 1073 and 2336 for 30°, 60° and 90° respectively. This feature is found to have high statistical significance for 30°-90° flexion and 60°-90° elbow flexion. The results shows that this method is useful in understanding muscle contractions at various angles of elbow joint. This technique can be extended to analyze muscles in neuromuscular conditions such as fatigue.

DIFFERENTIATING sEMG SIGNALS UNDER MUSCLE FATIGUE AND NON-FATIGUE CONDITIONS USING LOGISTIC REGRESSION CLASSIFIERS.

In this work, an attempt has been made to differentiate surface electromyography signals under fatigue and non-fatigue conditions. Signals are recorded from the biceps brachii muscles of 50 healthy volunteers. A well-established experimental protocol is followed for this purpose. Signals are subjected to further processing and features namely amplitude of first burst, myopulse percentage rate, Willison amplitude, power spectrum ratio and variance of central frequency are extracted. Three types of logistic regression classifiers, linear logistic, polykernel logistic regression and multinomial regression with ridge estimator are used for automated analysis. Classifier parameters are tuned to enhance the accuracy and performance indices of algorithms, and are compared. The results show distinct values for extracted features in fatigue conditions which are statistically significant (0.0027 = P = 0.03). All classifiers are found to be effective in demarcating the signals. The linear logistic regression algorithm provides 79% accuracy with 40 iterations. However, in the case of multinomial regression with ridge estimator, only 7 iterations are required to achieve 80% accuracy. The polykernel logistic regression algorithm (0.06 = ? = 0.1) also provides 80% accuracy but with a marginal increment (1 % to 4 %) for precision, recall and specificity compared to other two classifiers.

Analysis of Tcrvb8, Il4, and Ifg as genetic predisposition factors for atopic IgE response in a murine model.

Allergen-induced synthesis of lgE Abs in genetically predisposed individuals constitutes the hallmark of allergic diseases; however, the molecular basis of this genetic predisposition remains unknown. T cell cytokines lL-4 and IFN-gamma reciprocally regulate lgE synthesis and are potential genetic factors governing atopy. To examine the inheritance patterns of IgE responsiveness and address the role of these cytokines as genetic predisposition factors, in this study we established a MHC-identical mouse colony comprising crosses between two inbred strains of mouse, A.SW and SJL, respectively representing high and low IgE responder phenotypes. Segregation analysis with 149 [(A.SW x SJL)F1 x SJL] backcross and 148 [(A.SW x SJL)F1 x F1]F2 mice suggested that persistent high IgE responsiveness was inherited as a simple Mendelian dominant trait under the control of a single non-MHC, autosomal gene of major effect in these strains. Since SJL lacked Tcrvb8 genes, we examined the possibility of Tcrvb8 as a candidate gene for IgE responsiveness. The results suggested association of neither the Tcrvb8 gene nor its expression with allergen-induced IgE phenotype. Furthermore, microsatellite marker and gene sequencing analyses revealed that neither of the ll4 and lfg genes was associated with IgE phenotype. Moreover, correlation studies between IgE and cytokine levels in splenocyte cultures indicated that IgE levels were moderately to poorly correlated with IL-4 and IFN-gamma levels. It is concluded that even though expression of Tcrvb8, II4, and Ifg genes may play pivotal roles in IgE regulation, these genes per se do not contribute to genetic predisposition of allergen-induced IgE hyperresponsiveness in these strains of mice.

Arbitrary single short primers identify polymorphic DNA markers that distinguish inbred strains of mice.

The inbred mouse strains, Balb/c and SJL/J, have been widely used in biomedical research to unravel the genetic basis of susceptibility to tumors, viral diseases, autoimmune encephalitis, atopic disorders and neuro-retinopathies. In this study we attempted to identify DNA polymorphisms that distinguish them using RAPD assay. Screening of the genomic DNA of mice with a panel of 100 random decamer primers led to the identification of 36 primers which amplified 204 strain specific RAPD markers. On an average each of the selected primer amplified 11 bands of which 5.6 were strain specific. Segregation of RAPD markers in a (Balb/c x SJL/J) F1 x SJL/J backcross progeny (n = 6) suggested that the markers are potentially suitable for molecular genetic linkage studies.

Multiple mismatch annealing: basis for random amplified polymorphic DNA fingerprinting.

In order to investigate the possible mechanism underlying the random amplified polymorphic DNA (RAPD) fingerprinting, we examined the origin and the nucleotide sequences of RAPD bands. Our data suggest that a number of sites in the genome are flanked by perfect or imperfect invert repeats, which permit multiple mismatch-annealing to occur between the single primer and the template DNA and eventually lead to the exponential amplification of the encompassing DNA segments.

Determination of optimal in vitro conditions for caprine alternative complement pathway assay.

Optimal in vitro testing conditions for caprine alternative complement pathway assay were determined. Effects of the following variables were tested: heterologous erythrocytes; pH, ionic strength and Mg2+ ion concentration of the complement diluent; incubation time and temperature. Rabbit erythrocytes were the optimal target cells. The optimal buffer conditions were: pH 8.0, ionic strength 0.06 mmol NaCl and 5mmol Mg2+ ion. Optimal incubation time and temperature were 75 min and 30 degrees C, respectively.