Abstract:In this work, an attempt has been made to differentiate muscle non-fatigue and fatigue conditions using sEMG signals and texture representation of the timefrequency images. The sEMG signals are recorded from the biceps brachii muscle of 25 healthy adult volunteers during dynamic fatiguing contraction. The first and last curls of these signals are considered as the non-fatigue and fatigue zones, respectively. These signals are preprocessed and the time-frequency spectrum is computed using short time fourier transform (STFT). Gray-Level Co-occurrence Matrix (GLCM) is extracted from low (15-45 Hz), medium (46-95 Hz) and high (96-150 Hz) frequency bands of the time-frequency images. Further, the features such as contrast, correlation, energy and homogeneity are calculated from the resultant matrices. The results show that the high frequency band based features are able to differentiate non-fatigue and fatigue conditions. The features such as correlation, contrast and homogeneity extracted at angles 0°, 45°, 90°, and 135°are found to be distinct with high statistical significance (p < 0.0001). Hence, this framework can be used for analysis of neuromuscular disorders.

An attempt has been made in this Letter to analyse term (week of gestation (WOG) >37) and preterm (WOG ≤ 37) conditions using uterine electromyography (uEMG) signals and generalised Hurst exponent (GHE) features. For this analysis, public database signals recorded from the surface of abdomen are considered. Multifractal detrended fluctuation analysis is performed on the signals and the GHE is calculated. From the exponent, seven features are extracted and data-balancing based on synthetic minority over-sampling technique is used to retain a balanced feature contribution by the term and preterm records. Two classification algorithms namely, Naive Bayes and logistic regression (LR) are employed to classify the signals. Tenfold cross validation approach is executed and the performance is validated using accuracy, precision and recall. The results show the uEMG signals exhibit multifractal characteristics and five GHE features are significant in distinguishing the term and preterm uEMG signals. The LR classifier gives the highest accuracy of 97.8%. Therefore, it appears that the multifractal Hurst exponent features in combination with LR classifier can be used as biomarkers for predicting the preterm or term delivery during the early stage of gestation.

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In this study, an attempt has been made to identify the origin of multifractality in uterine electromyography signals and to differentiate term (gestational age. 37 weeks) and preterm (gestational age 4 37 weeks) conditions by multifractal detrended moving average technique. The signals obtained from a publicly available database, recorded from the abdominal surface during the second trimester, are used in this study. The signals are preprocessed and converted to shuffle and surrogate series to examine the source of multifractality. Multifractal detrended moving average algorithm is applied on all the signals. The presence of multifractality is verified using scaling exponents, and multifractal spectral features are extracted from the spectrum. The variation of multifractal features in term and preterm conditions is analyzed statistically using Student's t-test. The results of scaling exponents show that the uterine electromyography or electrohysterography signals reveal multifractal characteristics in term and preterm conditions. Further investigation indicates the existence of long-range correlation as the primary source of multifractality. Among all extracted features, strength of multifractality, exponent index, and maximum and peak singularity exponents are statistically significant (p \ 0.05) in differentiating term and preterm conditions. The coefficient of variation is found to be lower for strength of multifractality and peak singularity exponent, which reveal that these features exhibit less inter-subject variance. Hence, it appears that multifractal analysis can aid in the diagnosis of preterm or term delivery of pregnant women.