The objective of this study was to compute reference SEMG values for normal subjects of 13 parameters extracted in the time, frequency and bispectrum domain, from the Biceps Brachii (BB) muscle generated under isometric voluntary contraction (IVC). SEMG signals were recorded from 94 subjects for 5s at 10, 30, 50, 70 and 100% of maximum voluntary contraction (MVC). The Wilcoxon signed rank test was applied to detect significant differences or not at p<0.05 between force levels for each of the 13 parameters. The main findings of this study can be summarized as follows: (i) The time domain parameters turns per second and number of zero crossings per second increase significantly with force level. (ii) The power spectrum median frequency parameter decreases significantly with force level, whereas maximum power and total power increase significantly with force level. (iii) The bispectrum parameter, maximum amplitude, increases significantly with force level with the exception the transition from 30% to 50% MVC. Although, the tests for Gaussianity and linearity show no significant difference with force level, the SEMG signal exhibits a more Gaussian distribution with increase of force up to 70% MVC. The SEMG linearity test, which is a measure of how constant the bicoherence index is in the bi-frequency domain, shows that the signal's bicoherence index is less constant (hence, the signal is less linear) at 70% of MVC compared to 10, 30, 50 and 100% MVC. (iv) The time domain parameters have good correlation between them as well as, between each one of them and maximum and total power. The median frequency has a good (negative) correlation with the bispectrum peak amplitude. (v) No significant differences exist between values based on gender or age. The findings of this study can further be used for the assessment of subjects suffering with neuromuscular disorders, or in the rehabilitation laboratory for monitoring the elderly or the disabled, or in the occupational medicine laboratory.
We introduce a novel method for an automatic classification of subjects to those with or without neuromuscular disorders. This method is based on multiscale entropy of recorded surface electromyograms (sEMGs) and support vector classification. The method was evaluated on a single-channel experimental sEMGs recorded from biceps brachii muscle of nine healthy subjects, nine subjects with muscular and nine subjects with neuronal disorders, at 10%, 30%, 50%, 70% and 100% of maximal voluntary contraction force. Leave-one-out cross-validation was performed, deploying binary (healthy/patient) and three-class classification (healthy/myopathic/neuropathic). In the case of binary classification, subjects were distinguished with 81.5% accuracy (77.8% sensitivity at 83.3% specificity). At three-class classification, the accuracy decreased to 70.4% (myopathies were recognized with a sensitivity of 55.6% at specificity 88.9%, neuropathies with a sensitivity of 66.7% at specificity 83.3%). The proposed method is suitable for fast and non-invasive discrimination of healthy and neuromuscular patient groups, but it fails to recognize the type of pathology.
The objective of this ongoing study is to investigate whether or not Bispectral analysis (BS), a particular form of higher order spectra (HOS), may be utilized for analyzing the surface electromyographic signal (SEMG). The bicoherence index was used for characterizing the Gaussianity of the signal. Results indicate that SEMG signal distribution is highly non-Gaussian at low and high levels of force whereas the distribution has its maximum Gaussianity at the mid level of maximum voluntary contraction (MVC), i.e. at 50%. A measure of the linearity of the signal, based on deciding whether or not the estimated bicoherence is constant, follows the reverse pattern with the measure of Gaussianity. The power spectrum's (PS) median frequency, decreases from 105 to 93 Hz with increase of force, whereas the number of turns and the number of zero crosses increase with force Further work is currently in progress in order to evaluate the usefulness of HOS in normal subjects and subjects suffering from neuromuscular disorders.
In this work, multi-scale amplitude modulation-frequency modulation (AM-FM) features are extracted from surface electromyographic (SEMG) signals and they are used for the classification of neuromuscular disorders. The method is validated on SEMG signals recorded from a total of 40 subjects: 20 normal and 20 abnormal cases (11 myopathy, and 9 neuropathy cases), at 10%, 30%, 50%, 70% and 100% of maximum voluntary contraction (MVC), from the biceps brachii muscle. For the classification, three classifiers are used: (i) the statistical K-nearest neighbor (KNN), (ii) the self-organizing map (SOM) and (iii) the support vector machine (SVM). For all classifiers, the leave-one-out methodology is used to validate the classification of the SEMG signals into normal or abnormal (myopathy or neuropathy). A classification success rate of 78% for the AM-FM features and SVM models was achieved. These results also show that SEMG can be used as a non-invasive alternative to needle EMG for differentiating between normal and abnormal (myopathy, or neuropathy) cases.
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