Ramakrishnan Swaminathan

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. © 2021 European Federation for Medical Informatics (EFMI) and IOS Press.

The measurement and analysis of the electrical activity of muscle provide information that aids in the control of assistive devices. The investigation of these signals under varied physiological conditions, such as fatigue, enables reliable control. Muscle fatigue is a muscular condition associated with loss of muscle function. The early detection of muscle fatigue using surface Electromyography (sEMG)-based electrical measurements is challenging due to the nonlinear variations of the signal. In this work, an attempt has been made to understand the effect of dynamic nonlinear variations in the characteristics of the signal to develop a reliable fatigue index. The methodology involves the acquisition of myoelectric signals from the biceps brachii muscle of 52 healthy participants during dynamic contractions. The acquired signals are preprocessed and are analyzed with symbolic transition networks. Features such as symbolic entropy, network entropy, uniformity, and, minimum and maximum effective degrees (EDs) are extracted for further analysis. Appropriate decision boundaries are established for each feature using receiver operator characteristics (ROCs) and machine learning algorithms. The results indicate a decrease in signal complexity with fatigue. All the extracted features show a statistically significant difference (p < 0.05) between both conditions. Symbolic entropy achieves an accuracy of 89%, and the maximum ED yields an accuracy of 90% based on thresholds estimated with ROC. Furthermore, only a marginal improvement is observed with the combination of these features and the Naive Bayes classifier. It appears that the proposed maximum ED could be used as a reliable fatigue index in real-time applications for the improvement of rehabilitation efficacy.

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. © 2021 European Federation for Medical Informatics (EFMI) and IOS Press.

Muscle fatigue is a neuromuscular disease which occurs when the muscles fail to produce the necessary or expected potential, which is the cause for muscular forces. It could be either due to overexertion of the muscles or any excessive repetitive action [1]. It can occur to any subject - both normal as well as abnormal. The most common symptoms of this condition are localized pain, muscle twitching, trembling and muscle cramps. The detection of muscle fatigue can assist in the improvement of the performance in many fields such as clinical diagnosis and sports biomechanics and facilitates in the commercial development of various industries [2], [3]. Analysis of fatigue conditions of a muscle also plays a major role in the rehabilitation processes and kinesiology [4]. The behaviour of sEMG signals is different under non-fatigue and fatigue conditions due to energetic, metabolic, and structural variations in the muscle [5]. Hence, in this work, the differences in the muscle activity under non-fatigue and fatigue conditions as determined from recorded multichannel sEMG signals of biceps brachii and triceps brachii during dynamic contractions are studied.

In this study, an attempt has been made to evaluate the applicability of geometric features extracted from the different frequency bands of surface electromyography (sEMG) signals for detecting muscle fatigue condition. For this purpose, sEMG signals are acquired from twenty-five healthy volunteers during isometric contraction of biceps brachii muscle. The nonfatigue and fatigue segments are obtained from preprocessed signals and are separated into low frequency band (LFB: 15- 45Hz), medium frequency band (MFB: 55-95Hz) and high frequency band (HFB: 95-500Hz). The analytical representations of these signals are obtained from Hilbert Transform and the features, area and perimeter are extracted from the resultant shape. The results demonstrate that the features obtained from the three bands can differentiate nonfatigue and fatigue conditions with significant difference (p<0.05). Among the three bands, LFB achieves high sensitivity of 88% and 84% for perimeter and area feature respectively. However, sensitivity in MFB and HFB is decreased for both the features. It appears that the geometric features associated with LFB signals are more sensitive in detecting fatigue. It is interesting to note that the sensitivity is in acceptable level for low-frequency signals (15- 45Hz). However, the study has to be conducted on large population to draw a reliable conclusion.

Surface electromyography (sEMG) signals are stochastic, multicomponent and non-stationary, and therefore their interpretation is challenging. In this study, an attempt has been made to develop an automated muscle fatigue detection system using variational mode decomposition (VMD) features of sEMG signals and random forest classifier. The sEMG signals are acquired from 103 healthy volunteers during isometric (45 subjects) and dynamic (58 subjects) muscle fatiguing contractions and preprocessed. The band-limited intrinsic mode functions (BLIMFs) are extracted from non-fatigue and fatigue segments of the signals using the VMD algorithm. Hjorth features, such as activity, mobility and complexity are extracted from each BLIMF and are given to the random forest classifier. The performance of these features is evaluated using leave-one-subject-out cross-validation. The results show that the complexity feature performs better than others and it has resulted in an accuracy of 83% in dynamic contractions and 80% in isometric contractions. The performance is increased by about 8% in a dynamic condition when the most significant complexity features (p < 0.001) are used and by about 12% for isometric when the authors use all significant features. Therefore, the proposed approach could be used to detect fatigue conditions in various neuromuscular activities and real-time monitoring in the workplace.

Surface electromyography (sEMG) is a non-invasive technique to assess the electrical activity of contracting skeletal muscles. sEMG-based muscle fatigue detection plays a key role in sports medicine, ergonomics and rehabilitation. These signals are random, multicomponent, nonlinear and the degree of fluctuations is higher in dynamic contractions. Hence, the extraction of reliable biomarkers remains a challenging task. In this work, an attempt has been made to differentiate non-fatigue, and fatigue conditions using nonlinear techniques, namely, binary and weighted Visibility Graph (VG) features. For this, signals are recorded from the biceps brachii muscle of 52 healthy adult volunteers. These signals are preprocessed, and the contractions associated with the non-fatigue and fatigue conditions are segmented. The graph transformation is performed, and first-order and second-order statistics, along with entropy measures, are extracted from the degree distribution. Parametric and non-parametric machine learning methods are applied for the classification. The results show that the proposed VG approach is able to capture the fluctuations of the signals in non-fatigue and fatigue conditions. Further, all extracted features exhibit a significant difference with p <0.05. Maximum accuracy of 89.1% is achieved with information gain selected features and extreme learning machines classifier. Additionally, weighted VG features perform better than the binary version with a difference in the accuracy of 5%. It appears that the proposed approach could be used in real-time implementation for the monitoring of muscle fatigue conditions.

Accurate prediction of preterm birth is a global, public health priority. This necessitates the need for an efficient technique that aids in early diagnosis. The objective of this study is to develop an automated system for an effective detection of preterm (weeks of gestation < 37) condition using Electrohysterography (EHG) and topological features associated with the frequency components of signals. The EHG signals recorded prior to gestational age of 26 weeks are considered. The pre-processed signals are subjected to discrete Fourier transform to obtain the Fourier coefficients. The envelope is computed from the boundary of the complex Fourier coefficients identified using the α-shape method. Topological features namely, area, perimeter, circularity, convexity, ellipse variance and bending energy are extracted from the envelope. Classifications based on threshold-determination method and machine learning algorithms namely, naïve Bayes, decision tree and random forest are employed to differentiate the term and preterm conditions. The results show that the Fourier coefficients of EHG signals exhibit different shapes in the term and preterm conditions. The regularity of signals is found to increase in preterm condition. All the features are found to have significant differences between these two conditions. Bending energy as a single biomarker achieves a maximum accuracy of 80.7%. The random forest model based on the topological features detects the conditions with the maximum accuracy and positive predictive value of about 98.6%. Therefore, the proposed automated system seems to be effective and could be used for the accurate detection of term and preterm conditions.

Surface electromyography (sEMG) is a technique which noninvasively acquires the electrical activity of muscles and is widely used for muscle fatigue assessment. This study attempts to characterize the dynamic muscle fatiguing contractions with frequency bands of sEMG signals and a geometric feature namely the instantaneous spectral centroid (ISC). The sEMG signals are acquired from biceps brachii muscle of fifty-eight healthy volunteers. The frequency components of the signals are divided into low frequency band (10-45Hz), medium frequency band (55-95Hz) and high frequency band (95-400Hz). The signals associated with these bands are subjected to a Hilbert transform and analytical shape representation is obtained in the complex plane. The ISC feature is extracted from the resultant shape of the three frequency bands. The results show that this feature can differentiate the muscle nonfatigue and fatigue conditions (p<0.05). It is found the values of ISC is lower in fatigue conditions irrespective of frequency bands. It is also observed that the coefficient of variation of ISC in the low frequency band is less and it demonstrates the ability of handling inter-subject variations. Therefore, the proposed geometric feature from the low frequency band of sEMG signals could be considered for detecting muscle fatigue in various neuromuscular conditions.