Cyclostationary analysis of uterine EMG measurements for the prediction of preterm birth.

Preterm birth (gestational age < 37 weeks) is a public health concern that causes fetal and maternal mortality and morbidity. When this condition is detected early, suitable treatment can be prescribed to delay labour. Uterine electromyography (uEMG) has gained a lot of attention for detecting preterm births in advance. However, analyzing uEMG is challenging due to the complexities associated with inter and intra-subject variations. This work aims to investigate the applicability of cyclostationary characteristics in uEMG signals for predicting premature delivery. The signals under term and preterm situations are considered from two online datasets. Preprocessing is carried out using a Butterworth bandpass filter, and spectral correlation density function is adapted using fast Fourier transform-based accumulation method (FAM) to compute the cyclostationary variations. The cyclic frequency spectral density (CFSD) and degree of cyclostationarity (DCS) are quantified to assess the existence of cyclostationarity. Features namely, maximum cyclic frequency, bandwidth, mean cyclic frequency (MNCF), and median cyclic frequency (MDCF) are extracted from the cyclostationary spectrum and analyzed statistically. uEMG signals exhibit cyclostationarity property, and these variations are found to distinguish preterm from term conditions. All the four extracted features are noted to decrease from term to preterm conditions. The results indicate that the cyclostationary nature of the signals can provide better characterization of uterine muscle contractions and could be helpful in detecting preterm birth. The proposed method appears to aid in detecting preterm birth, as analysis of uterine contractions under preterm conditions is imperative for timely medical intervention.

Empirical Mode Decomposition Based Measures for Investigating the Progression of Pregnancy from Uterine EMG.

The objective of this study is to analyze the uterine electromyography (uEMG) signals to study the progression of pregnancy under term condition (gestational age > 36 weeks) using EMD-based time-frequency features. uEMG signals are obtained from the multiple public datasets during two conditions, namely T1 (acquired < 26 gestational weeks) and T2 (acquired ≥ 26 gestational weeks). The considered signals are preprocessed. Empirical mode decomposition is applied to decompose the signals and time-frequency features, such as median frequency (MDF), mean frequency (MNF), peak frequency and peak magnitude, are extracted from each intrinsic mode functions and statistically analyzed. The results depict that the obtained time-frequency features are able to distinguish between T1 and T2 conditions. The extracted features, namely MNF and MDF, are observed to decrease from T1 to T2 conditions. These features are found to have higher effect size, confirming the better differentiation between T1 and T2 conditions. It appears that EMD-based time-frequency features can aid in studying the evolving changes in uterine contractions towards labor.

Diversity and impacts of macroalgae and cyanobacteria on multi-stressed coral reefs in the Gulf of Mannar Marine Biosphere Reserve.

In India, intertidal seaweed resources are widely investigated and utilized for various applications, whereas reef-associated seaweed resources and their impacts on corals are lesser known. Thus, the present study investigated the diversity and impacts of macroalgae and cyanobacteria on coral reefs distributed in 21 islands under the Gulf of Mannar Marine Biosphere Reserve (GoMMBR), Tamil Nadu. About 140 macroalgal species representing 53 species of Chlorophyta, 32 species of Ochrophyta (Phaeophyta), and 55 species of Rhodophyta were recorded. Only three cyanobacterial species were documented during this study. All the documented species were categorized as edible, medicinal, smothering, bloom-forming, sediment trapping, and auxiliary. Diversity indices and multivariate analysis indicated latitudinal gradient distribution of macroalgae, where the maximum diversity was observed from the Mandapam group of Islands. The predominant genera observed in all the islands were Caulerpa, Halimeda, Turbinaria, and Sargassum. The updated checklist of seaweeds and cyanobacteria of India revealed 1118 and 258 species, correspondingly, on Indian coasts, including coral reef regions. The use of traditional morphology-based techniques in this study without molecular approaches to identify all of the specimens limits our investigation. Thus, molecular taxonomy is necessary to revalidate and confirm the actual genetic diversity existing in the Indian waters. Results of this study would benefit the scientific community and industries in various aspects, such as molecular taxonomy, biomass utilization, reef conservation, and industrial applications.

Automated Detection of Seizure Types from the Higher-Order Moments of Maximal Overlap Wavelet Distribution.

In this work, an attempt has been made to develop an automated system for detecting electroclinical seizures such as tonic-clonic seizures, complex partial seizures, and electrographic seizures (EGSZ) using higher-order moments of scalp electroencephalography (EEG). The scalp EEGs of the publicly available Temple University database are utilized in this study. The higher-order moments, namely skewness and kurtosis, are extracted from the temporal, spectral, and maximal overlap wavelet distributions of EEG. The features are computed from overlapping and non-overlapping moving windowing functions. The results show that the wavelet and spectral skewness of EEG is higher in EGSZ than in other types. All the extracted features are found to have significant differences (p < 0.05), except for temporal kurtosis and skewness. A support vector machine with a radial basis kernel designed using maximal overlap wavelet skewness yields a maximum accuracy of 87%. In order to improve the performance, the Bayesian optimization technique is utilized to determine the suitable kernel parameters. The optimized model achieves the highest accuracy of 96% and an MCC of 91% in three-class classification. The study is found to be promising, and it could facilitate the rapid identification process of life-threatening seizures.

Defining the caprine γδ T cell WC1 multigenic array and evaluation of its expressed sequences and gene structure conservation among goat breeds and relative to cattle.

Workshop cluster 1 (WC1) molecules are part of the scavenger receptor cysteine-rich (SRCR) superfamily and act as hybrid co-receptors for the γδ T cell receptor and as pattern recognition receptors for binding pathogens. These members of the CD163 gene family are expressed on γδ T cells in the blood of ruminants. While the presence of WC1+ γδ T cells in the blood of goats has been demonstrated using monoclonal antibodies, there was no information available about the goat WC1 gene family. The caprine WC1 multigenic array was characterized here for number, structure and expression of genes, and similarity to WC1 genes of cattle and among goat breeds. We found sequence for 17 complete WC1 genes and evidence for up to 30 SRCR a1 or d1 domains which represent distinct signature domains for individual genes. This suggests substantially more WC1 genes than in cattle. Moreover, goats had seven different WC1 gene structures of which 4 are unique to goats. Caprine WC1 genes also had multiple transcript splice variants of their intracytoplasmic domains that eliminated tyrosines shown previously to be important for signal transduction. The most distal WC1 SRCR a1 domains were highly conserved among goat breeds, but fewer were conserved between goats and cattle. Since goats have a greater number of WC1 genes and unique WC1 gene structures relative to cattle, goat WC1 molecules may have expanded functions. This finding may impact research on next-generation vaccines designed to stimulate γδ T cells.

Analysis of Facial Electromyography Signals Using Linear and Non-Linear Features for Human-Machine Interface.

In this work, an attempt has been made to analyze the facial electromyography (facial EMG) signals using linear and non-linear features for the human-machine interface. Facial EMG signals are obtained from the publicly available, widely used DEAP dataset. Thirty-two healthy subjects volunteered for the establishment of this dataset. The signals of one positive emotion (joy) and one negative emotion (sadness) obtained from the dataset are used for this study. The signals are segmented into 12 epochs of 5 seconds each. Features such as sample entropy and root mean square (RMS) are extracted from each epoch for analysis. The results indicate that facial EMG signals exhibit distinct variations in each emotional stimulus. The statistical test performed indicates statistical significance (p<0.05) in various epochs. It appears that this method of analysis could be used for developing human-machine interfaces, especially for patients with severe motor disabilities such as people with tetraplegia.

Detection of Tonic-Clonic Seizures using Wavelet Entropy of Scalp EEG.

Epilepsy is the most common chronic neurologic disorder characterized by the recurrence of unprovoked seizures. These seizures are paroxysmal events that result from abnormal neuronal discharges and are categorized into various types based on the clinical manifestations and localization. Tonic-Clonic seizures (TCSZ) may lead to injuries, and constitute the major risk factor for sudden unexpected death in epilepsy (SUDEP), especially in unattended patients. Therapeutic decisions and clinical trials rely on Video EEG which is not practical outside of clinical setting. In this study, wavelet entropy of scalp EEG signals are utilized to discriminate the seizures with and without clinical manifestations. The scalp EEG records from the publically available Temple University Hospital (TUH) dataset are considered for this work. A sevenlevel, fourth order Daubechies (db4) wavelet is utilized for the decomposition of first four seconds of scalp EEG during seizures. The entropy is extracted from the resultant coefficients and are used to develop SVM based models. Most of the extracted features found to have significant differences (p<0.05). The results show that polynomial SVM model achieves an accuracy of 95.5%, positive predictive value (PPV) of 99.4%, negative predictive value (NPV) of 91.57% and F-Score of 95.9%. Therefore, the proposed approach could be a support in detecting life-threatening seizures.

Emotion Recognition Using Spectral Feature from Facial Electromygraphy Signals for Human-Machine Interface.

Recognition of the emotions demonstrated by human beings plays a crucial role in healthcare and human-machine interface. This paper reports an attempt to classify emotions using a spectral feature from facial electromyography (facial EMG) signals in the valence affective dimension. For this purpose, the facial EMG signals are obtained from the DEAP dataset. The signals are subjected to Short-Time Fourier Transform, and the peak frequency values are extracted from the signal in intervals of one second. Support vector machine (SVM) classifier is used for the classification of the features extracted. The extracted feature can classify the signals in the valence dimension with an accuracy of 61.37%. The proposed feature could be used as an added feature for emotion recognition, and this method of analysis could be extended to myoelectric control applications.

Analysis of Frequency Bands of Uterine Electromyography Signals for the Detection of Preterm Birth.

In this work, an attempt has been made to analyze the influence of the frequencies bands in uterine electromyography (uEMG) signals on the detection of preterm birth. The signals recorded from the women's abdomen during pregnancy are considered in this study. The signals are subjected to preprocessing using digital bandpass Butterworth filter and decomposed into different frequency bands namely, 0.3-1.0 Hz (F1), 1.0-2.0 Hz (F2) and 2.0-3.0Hz (F3). Spectral features namely, peak magnitude, peak frequency, mean frequency and median frequency are extracted from the power spectrum. Classification models namely, k-nearest neighbor, support vector machine and random forest are employed to distinguish the term and preterm conditions. The results show that the features extracted from these frequency bands are able to differentiate term and preterm condition. Particularly, the frequency band F3 performs better than other frequency bands. The features associated with these frequencies along with random forest classification model achieves a maximum accuracy of 75.2%. Thus, these measures could be used to accurately detect the preterm birth well in advance.

Characterization of the domestic goat γδ T cell receptor gene loci and gene usage.

Goats and cattle diverged 30 million years ago but retain similarities in immune system genes. Here, the caprine T cell receptor (TCR) gene loci and transcription of its genes were examined and compared to cattle. We annotated the TCR loci using an improved genome assembly (ARS1) of a highly homozygous San Clemente goat. This assembly has already proven useful for describing other immune system genes including antibody and leucocyte receptors. Both the TCRγ (TRG) and TCRδ (TRD) loci were similarly organized in goats as in cattle and the gene sequences were highly conserved. However, the number of genes varied slightly as a result of duplications and differences occurred in mutations resulting in pseudogenes. WC1+ γδ T cells in cattle have been shown to use TCRγ genes from only one of the six available cassettes. The structure of that Cγ gene product is unique and may be necessary to interact with WC1 for signal transduction following antigen ligation. Using RT-PCR and PacBio sequencing, we observed the same restriction for goat WC1+ γδ T cells. In contrast, caprine WC1+ and WC1- γδ T cell populations had a diverse TCRδ gene usage although the propensity for particular gene usage differed between the two cell populations. Noncanonical recombination signal sequences (RSS) largely correlated with restricted expression of TCRγ and δ genes. Finally, caprine γδ T cells were found to incorporate multiple TRD diversity gene sequences in a single transcript, an unusual feature among mammals but also previously observed in cattle.

Gene characterization and expression of the γδ T cell co-receptor WC1 in sheep.

Sheep are known to express the hybrid co-receptor/pattern recognition receptor WC1 on their γδ T cells but details of the ovine WC1 multigenic array and gene expression were unknown. Annotation of the sheep genome assembly (Oar_rambouillet_v1.0) yielded 15 complete and 42 partial WC1 genes predicted to code for six different protein structures. RT-PCR amplification of the most distal scavenger receptor cysteine rich (SRCR) domain known as a1, which serves as the gene signature, from genomic and cDNA templates verified the majority of annotated genes. As for cattle and goats, sheep a1 domain sequences included WC1.1 and WC1.2 types. A unique ovine gene, WC1-16, had multiple SRCR a-pattern domains in tandem similar to one found in goats. Intracytoplasmic domains of WC1 transcripts had splice variants that may affect signal transduction. The larger number of WC1 genes in sheep and differences in structures and splice variants relative to cattle could have implications in expression patterns and engagement of γδ T cells by pathogens or vaccine constructs.

Microbial diversity from the continental shelf regions of the Eastern Arabian Sea: A metagenomic approach.

The marine microbiome is a complex and least-understood habitat, which play a significant role in global biogeochemical cycles. The present study reported the culture-independent assessment of microbial diversity from the Arabian Sea (AS) sediments (from Gujarat to Malabar; at 30 m depth) by using metagenome sequence analysis. Our results elucidated that bacterial communities in the Malabar coastal region are highly diverse than the Gujarat coast. Moreover, Statistical analysis (Spearman rank correlation) showed a significant correlation co-efficient value (r = P < 0.001) between microbial communities and physicochemical parameters (salinity and dissolved oxygen) in the water column. A total of 39 bacterial phyla were recorded from the eastern side of AS, of which six phyla Proteobacteria, Bacteroidetes, Actinobacteria, Cyanobacteria, Firmicutes, and Planctomycetes were found to be the most dominant group. The most dominant genus from Valapad region (Malabar Coast) was found to be Halomonas sp., while other regions were dominated with Psychrobacter pulmonis. The subsequent Principal Coordinate Analysis (PCoA) showed 99.53% variance, which suggests that, highly distinct microbial communities at Valapad (Malabar Coast) sampling location than other sites. Moreover, the microbial metabolic activity analysis revealed the important functions of microbial communities in the AS are hydrocarbon degradation, polymer degradation, nutrient oxidation and sulphate reduction (biodegradation process). Further extended studies are needed to be carried out for better understanding the functional diversity of microbial communities from the marine sediments.

Automated detection of subthalamic nucleus in deep brain stimulation surgery for Parkinson's disease using microelectrode recordings and wavelet packet features.

BACKGROUND: Deep brain stimulation (DBS) to the subthalamic nucleus (STN) is an effective neurosurgery that overcomes the motor system alternations of patients with advanced Parkinson's disease. The most challenging aspect of DBS surgery is the accurate identification of STN and its borders. In general, it is performed manually by a neurophysiologist using the microelectrode recordings (MERs). This process is subjective, and tedious and further, interpretation of MERs is difficult because of its inherent nonstationary variations. NEW METHODS: In this work, the wavelet-packet based features are proposed to automatically localize the STN and its subcortical structures using microelectrode recorded signals during DBS surgery. The study analyses 2904 MERs of 26 PD patients who underwent DBS implantation. The low and high order statistical parameters are extracted from the wavelet packet coefficients of MERs and used in the classifications, namely, non-STN vs. STN, pre-STN vs. STN and STN vs. post-STN. RESULTS: Most of the features are significantly different in STN and its subcortical regions, namely, pre-STN and post-STN. The proposed features achieve an average accuracy of 85 % in non-STN vs. STN, 87.2 % in pre-STN vs. STN and 77.7 % in STN vs. post-STN. The accuracy is improved by around 10 % in non-STN vs. STN and STN vs. post-STN when the transition error is 1 mm. COMPARISON WITH EXISTING METHODS: The proposed features are found to be better than the wavelet features. CONCLUSIONS: The proposed approach could be a potential useful adjunct for the real-time rapid intraoperative identification of STN and its anatomical borders.

Could we have missed out the seizure onset: A study based on intracranial EEG.

OBJECTIVE: Intracranial EEG covers only a small fraction of brain volume and it is uncertain if a discharge represents a true seizure onset or results from spread. We therefore assessed if there are differences between characteristics of the ictal onset when we are likely to have a true onset, and characteristics of the discharge in regions of spread. METHODS: Wavelet based statistical features were extracted in 503 onset and 390 spread channels of 58 seizures from 20 patients. These features were used as predictors in models based on machine learning algorithms such as k-nearest neighbour, logistic regression, multilayer perceptron, support vector machine, random and rotation forest. RESULTS: Statistical features (mean, variance, skewness and kurtosis) associated with all wavelet scales were significantly higher in onset than in spread channels. The best classifier, random forest, achieved accuracy of 79.6% and precision of 82%. CONCLUSIONS: The signals associated with onset and spread regions exhibit different characteristics. The proposed features are able to classify the signals with good accuracy. SIGNIFICANCE: Using our classifier on new seizures could help clinicians gain confidence in having recorded the real seizure onset or on the contrary be concerned that the true onset may have been missed.

Detection of Subthalamic Nucleus using Time-Frequency Features of Microelectrode recordings and Random Forest Classifier.

Accurate localization of subthalamic nucleus (STN) is a key prior in deep brain stimulation (DBS) surgery for the patients with advanced Parkinson's disease (PD). Microelectrode recordings (MERs) along with preplanned trajectories are often employed for the STN localization and it remains challenging task. These MER signals are nonstationary and multicomponent in nature. In this study, we propose a system based on time-frequency features of MERs to differentiate the STN and non-STN regions. We assessed the system with 50 MER trajectories from 26 PD patients who have undergone DBS surgery. The signals are pre-processed and subjected to six-level wavelet decomposition. Then, the entropy is computed from the detailed and approximate coefficients. These features are fed to the random forest classifier and the model is evaluated by leave one patient out cross-validation. The results show that entropy associated with detailed wavelet coefficients (D1and D2) are higher in STN where as it is lower in other wavelet scales. All extracted features except entropy from approximate coefficients are found to have significant difference between non-STN and STN (p<; 0.05). The random forest classifier achieves about 83% accuracy and 87% precision in differentiating the STN and non-STN regions.

Prediction of secondary generalization from a focal onset seizure in intracerebral EEG.

OBJECTIVE: We propose a system based on the first five seconds of intracerebrally recorded focal seizures to predict their evolution from focal to bilateral tonic-clonic seizure (FTC), to spread outside the onset zone but without tonic-clonic component (FS), or to a seizure remaining focal (F). METHODS: Nineteen time and frequency domain features were extracted from 39 seizures of 32 patients and were subjected to support vector machine based classification. Three prediction approaches regarding seizure evolution were (1) FTC vs FS vs F, (2) FTC vs FS or F and (3) FTC or FS vs F. RESULTS: We used 21 seizures for training. Most features had significantly different distributions in the three seizure types (p < 0.05). Eighteen seizures were used for testing. We predicted the evolution of 12 seizures in FTC vs FS vs F, 13 seizures in FTC vs FS or F and 14 seizures in FTC or FS vs F. CONCLUSION: The first five seconds of a focal seizure contains information regarding the eventual evolution of the seizure, which could be predicted in most seizures. SIGNIFICANCE: The proposed system could alert the health care team when a patient is hospitalized for intracerebral EEG and improve safety and eventually result in an implantable device.

Surface electromyography based muscle fatigue detection using high-resolution time-frequency methods and machine learning algorithms.

BACKGROUND AND OBJECTIVE: Surface electromyography (sEMG) based muscle fatigue research is widely preferred in sports science and occupational/rehabilitation studies due to its noninvasiveness. However, these signals are complex, multicomponent and highly nonstationary with large inter-subject variations, particularly during dynamic contractions. Hence, time-frequency based machine learning methodologies can improve the design of automated system for these signals. METHODS: In this work, the analysis based on high-resolution time-frequency methods, namely, Stockwell transform (S-transform), B-distribution (BD) and extended modified B-distribution (EMBD) are proposed to differentiate the dynamic muscle nonfatigue and fatigue conditions. The nonfatigue and fatigue segments of sEMG signals recorded from the biceps brachii of 52 healthy volunteers are preprocessed and subjected to S-transform, BD and EMBD. Twelve features are extracted from each method and prominent features are selected using genetic algorithm (GA) and binary particle swarm optimization (BPSO). Five machine learning algorithms, namely, naïve Bayes, support vector machine (SVM) of polynomial and radial basis kernel, random forest and rotation forests are used for the classification. RESULTS: The results show that all the proposed time-frequency distributions (TFDs) are able to show the nonstationary variations of sEMG signals. Most of the features exhibit statistically significant difference in the muscle fatigue and nonfatigue conditions. The maximum number of features (66%) is reduced by GA and BPSO for EMBD and BD-TFD respectively. The combination of EMBD- polynomial kernel based SVM is found to be most accurate (91% accuracy) in classifying the conditions with the features selected using GA. CONCLUSIONS: The proposed methods are found to be capable of handling the nonstationary and multicomponent variations of sEMG signals recorded in dynamic fatiguing contractions. Particularly, the combination of EMBD- polynomial kernel based SVM could be used to detect the dynamic muscle 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 biceps brachii sEMG signal using Multiscale Fuzzy Approximate Entropy.

In this work, an attempt has been made to differentiate sEMG signals under muscle fatigue and non-fatigue conditions using multiscale features. Signals are recorded from biceps brachii muscle of 50 normal adults during repetitive dynamic contractions. After preprocessing, the signal is divided into six segments, out of which first and last segments are considered for this analysis. Fuzzy Approximate Entropy (fApEn) is computed for each subject in the time scales ranging from 1 to 10. Features such as median, low scale median and high scale median are extracted from Multiscale Fuzzy Approximate Entropy (MSfApEn) and used for further analysis. The results show an increase in amplitude of the sEMG signals under fatigue condition. The MSfApEn values are higher in the case of nonfatigue indicating more complexity. The features extracted for the series are effective in differentiating the two conditions. The statistical t test performed indicated high statistical significance (p-value <;<;0.001) It appears that this method of analysis can be used for clinical evaluation of muscles.