Normalised Mutual Information of High-Density Surface Electromyography during Muscle Fatigue

Muscle Network Connectivity Study in Diabetic Peripheral Neuropathy Patients

Diabetic peripheral neuropathy (DPN) is a prevalent complication of chronic diabetes mellitus and has a significant impact on quality of life. DPN typically manifests itself as a symmetrical, length-dependent sensorimotor polyneuropathy with severe effects on gait. Surface electromyography (sEMG) is a valuable low-cost tool for assessing muscle activation patterns and precise identification of abnormalities. For the present study, we used information theory methods, such as cross-correlation (CC), normalized mutual information (NMI), conditional granger causality (CG-Causality), and transfer entropy (TE), to evaluate muscle network connectivity in three population groups: 33 controls (healthy volunteers, CT), 10 diabetic patients with a low risk of DPN (LW), and 17 moderate/high risk patients (MH). The results obtained indicated significant alterations in the intermuscular coupling mechanisms due to diabetes and DPN, with the TE group showing the best performance in detecting differences. The data revealed a significant increase in information transfer and muscle connectivity in the LW group over the CT group, while the MH group obtained significantly lower values for these metrics than the other two groups. These findings highlight the sEMG coupling metrics' potential to reveal neuromuscular mechanisms that could aid the development of targeted rehabilitation strategies and help monitor DPN patients.

Changes in normalized mutual information in response to strength training: An ancillary analysis of a quasi-randomized controlled trial

The aim of the present investigation was twofold. (1) to assess test-retest reliability of normalized mutual information (NMI) values extracted from the surface electromyography (sEMG) signal of muscles pairs of the upper body during dynamic bench press at a high load, and (2) to assess changes in NMI values from before to after a five-week quasi-randomized controlled bench press training intervention. For test-retest reliability, 20 strength trained males (age 25 ± 2 years, height 1.81 ± 0.07 m) performed two three-repetition maximum (3RM) tests in bench press, while sEMG was recorded from six upper body muscles. Tests were separated by 8.2 ± 2.9 days. For the training intervention, 17 male participants (age 26 ± 5 years, height 1.80 ± 0.07 m) trained bench press specific strength training for 5 weeks (TRA), while 13 male participants (age 23 ± 3 years, height 1.80 ± 0.08 m) constituted a control group (CON). 3RM bench press test and sEMG recordings were carried out before and after the intervention period. The NMI values ranged from poor to almost perfect reliability, with the majority displaying substantial reliability. TRA displayed a significant decrease in NMI values during the concentric phase for two agonist-agonist muscle pairs, while one agonist-agonist and two agonist-antagonist muscle pairs increased the NMI values during the eccentric phase. The observed changes did not exceed the minimal detectable threshold, and we therefore cannot surely ascertain that the changes observed in NMI values reflect genuine neural adaptations.

Age-Dependent Control of Shoulder Muscles During a Reach-and-Lift Task

Aging affects fatigability and is a risk factor for incurring a fatigue-related injury in the neck/shoulder region. Age-related changes in the electromyographical features of motor control may be partly responsible. Young (N = 17) and older (N = 13) adults completed a reach-and-lift task at their self-selected speed, before and after a fatiguing task targeting the neck/shoulder. Electromyography amplitude (root mean square), amplitude variability (root mean square coefficient of variation [CV]), functional connectivity (normalized mutual information [NMI]), and functional connectivity variability (NMI CV) were extracted from several muscles and analyzed for effects and interactions of age using general estimating equation models. Root mean square CV and deltoid NMI CV increased from pre- to postfatigue (ps < .05). Upper trapezius-deltoid NMI decreased for young, but increased for older adults, while the opposite response was found for lower trapezius-deltoid NMI (ps < .05). Older adults seem to adapt to fatigue in reach-and-lift movement with a cranial shift in control of the scapula.

Effects of Oral Branched-Chain Amino Acids (BCAAs) Intake on Muscular and Central Fatigue During an Incremental Exercise

The aim of this study was to investigate the effects of oral branched-chain amino acids (BCAAs) intake on muscular (creatine kinase and myoglobin) and central (serotonin) fatigue during an incremental exercise protocol and to determine the time to exhaustion. Sixteen male long-distance runners (25.7 ± 2.0 yrs) performed two trials, 14 days apart. Using a double-blind, placebo-controlled, randomised crossover design, participants ingested either 20 g of BCAAs (BCAA trial) or a placebo 1 hour prior to performing an incremental exercise session on a treadmill. The starting speed was 8 km/h and this was increased by 1 km/h every 5 minutes until volitional exhaustion. Blood analysis indicated that plasma levels of serotonin were lower in the BCAA trial (259.3 ± 13.5 ng/ml) than the placebo trial (289.1 ± 14.5 ng/ml) (p < 0.05). There was a similar pattern of results for free fatty acid (p < 0.05). The creatine kinase level was higher in the BCAA trial (346.1 ± 33.7 U/L) than the placebo trial (307.3 ± 30.2 U/L). No significant difference between trials was observed regarding the level of myoglobin (p = 0.139). Time to exhaustion was longer in the BCAA trial (50.4 ± 2.3 min) than the placebo trial (46.6 ± 3.2 min). In conclusion, oral intake of 20 g of BCAAs 1 hour prior to an incremental treadmill exercise session increased time to exhaustion, probably due to the reduction in serotonin concentration. As myoglobin levels were within the normal range in both trials, we conclude that the participants did not reach muscular fatigue.

Angle Estimation for Knee Joint Movement Based on PCA-RELM Algorithm

Surface electromyogram (sEMG) signals are easy to record and offer valuable motion information, such as symmetric and periodic motion in human gait. Due to these characteristics, sEMG is widely used in human-computer interaction, clinical diagnosis and rehabilitation medicine, sports medicine and other fields. This paper aims to improve the estimation accuracy and real-time performance, in the case of the knee joint angle in the lower limb, using a sEMG signal, in a proposed estimation algorithm of the continuous motion, based on the principal component analysis (PCA) and the regularized extreme learning machine (RELM). First, the sEMG signals, collected during the lower limb motion, are preprocessed, while feature samples are extracted from the acquired and preconditioned sEMG signals. Next, the feature samples dimensions are reduced by the PCA, as well as the knee joint angle system is measured by the three-dimensional motion capture system, are followed by the normalization of the feature variable value. The normalized sEMG feature is used as the input layer, in the RELM model, while the joint angle is used as the output layer. After training, the RELM model estimates the knee joint angle of the lower limbs, while it uses the root mean square error (RMSE), Pearson correlation coefficient and model training time as key performance indicators (KPIs), to be further discussed. The RELM, the traditional BP neural network and the support vector machine (SVM) estimation results are compared. The conclusions prove that the RELM method, not only has ensured the validity of results, but also has greatly reduced the learning train time. The presented work is a valuable point of reference for further study of the motion estimation in lower limb.

Complexity Measures of Voice Recordings as a Discriminative Tool for Parkinson's Disease

In this paper, we have investigated the differences in the voices of Parkinson's disease (PD) and age-matched control (CO) subjects when uttering three phonemes using two complexity measures: fractal dimension (FD) and normalised mutual information (NMI). Three sustained phonetic voice recordings, /a/, /u/ and /m/, from 22 CO (mean age = 66.91) and 24 PD (mean age = 71.83) participants were analysed. FD was first computed for PD and CO voice recordings, followed by the computation of NMI between the test groups: PD-CO, PD-PD and CO-CO. Four features reported in the literature-normalised pitch period entropy (Norm. PPE), glottal-to-noise excitation ratio (GNE), detrended fluctuation analysis (DFA) and glottal closing quotient (ClQ)-were also computed for comparison with the proposed complexity measures. The statistical significance of the features was tested using a one-way ANOVA test. Support vector machine (SVM) with a linear kernel was used to classify the test groups, using a leave-one-out validation method. The results showed that PD voice recordings had lower FD compared to CO (p < 0.008). It was also observed that the average NMI between CO voice recordings was significantly lower compared with the CO-PD and PD-PD groups (p < 0.036) for the three phonetic sounds. The average NMI and FD demonstrated higher accuracy (>80%) in differentiating the test groups compared with other speech feature-based classifications. This study has demonstrated that the voices of PD patients has reduced FD, and NMI between voice recordings of PD-CO and PD-PD is higher compared with CO-CO. This suggests that the use of NMI obtained from the sample voice, when paired with known groups of CO and PD, can be used to identify PD voices. These findings could have applications for population screening.

Slope Entropy: A New Time Series Complexity Estimator Based on Both Symbolic Patterns and Amplitude Information

The development of new measures and algorithms to quantify the entropy or related concepts of a data series is a continuous effort that has brought many innovations in this regard in recent years. The ultimate goal is usually to find new methods with a higher discriminating power, more efficient, more robust to noise and artifacts, less dependent on parameters or configurations, or any other possibly desirable feature. Among all these methods, Permutation Entropy (PE) is a complexity estimator for a time series that stands out due to its many strengths, with very few weaknesses. One of these weaknesses is the PE’s disregarding of time series amplitude information. Some PE algorithm modifications have been proposed in order to introduce such information into the calculations. We propose in this paper a new method, Slope Entropy (SlopEn), that also addresses this flaw but in a different way, keeping the symbolic representation of subsequences using a novel encoding method based on the slope generated by two consecutive data samples. By means of a thorough and extensive set of comparative experiments with PE and Sample Entropy (SampEn), we demonstrate that SlopEn is a very promising method with clearly a better time series classification performance than those previous methods.

Normalized Mutual Information of phonetic sound to distinguish the speech of Parkinson's disease

This study has investigated the use of inter-personnel mutual information computed from the phonetic sound recordings to differentiate between Parkinson's disease (PD) and control subjects. The normalized mutual information (NMI) denotes the amount of information shared between the voice recordings of people within the same group: PD and Control. The hypothesis of this study was that within group NMI will be significantly different when compared with inter- group NMI. For each phonetic sound, the NMI was computed for every pairing of recordings for both the PD and control groups. Pearson correlation coefficient analysis was used to determine the association of NMI with clinical parameters including Unified Parkinson's Disease Rating Scale (UPDRS), Montreal cognitive assessment (MoCA) and disease duration. ANOVA test for the three phonetic sounds of control and PD subjects showed that there is significant difference between the intra-group mean NMI for the two groups (p <; 0.003) and also showed significant association with the UPDRS motor examination score, MoCA and disease duration.

Identifying Noisy Electrodes in High Density Surface Electromyography Recordings Through Analysis of Spatial Similarities

In this study we developed a technique for identifying noisy electrodes in high density surface electromyography (HD-sEMG). The technique finds the spatial similarity of each electrode in the electrode array by counting the number of interactions the electrode has. Using this information the technique identifies noisy electrodes by finding electrodes that are significantly dissimilar to the other electrodes. The HD-sEMG recordings used in this study were taken from three participants who performed two isometric contractions of their biceps at 40% and 80% of their maximum voluntary contraction (MVC) force. White Gaussian noisy was added to a varying number of recorded signals before being digital filtering to generate a variety of recordings to test the technique with. In the recordings, groups of 2, 4, 8, and 16 electrodes had noise added such that the signal to noise ratios (SNR) were 0, 5, 10, 15, and 20dB. The results show that the technique can reliably identify groups of 2, 4, and 8 noisy electrodes with SNRs of 0, 5, and 10dB.

Relationship between Isometric Muscle Force and Fractal Dimension of Surface Electromyogram

The relationship between fractal dimension of the surface electromyogram (sEMG) and the intensity of muscle contraction is still controversial in simulated and experimental conditions. To support the use of fractal analysis to investigate myoelectric fatigue, it is crucial to establish the interdependence between fractal dimension and muscle contraction intensity. We analyzed the behavior of fractal dimension, conduction velocity, mean frequency, and average rectified value in twenty-eight volunteers at nine levels of isometric force. sEMG was obtained using bidimensional arrays in the biceps brachii muscle. The values of fractal dimension and mean frequency increased with force unless a plateau was reached at 30% maximal voluntary contraction. Overall, our findings suggest that, above a certain level of force, the use of fractal dimension to evaluate the myoelectric manifestations of fatigue may be considered, regardless of muscle contraction intensity.