Fuzzy Approximate Entropy Analysis of Chaotic and Natural Complex Systems: Detecting Muscle Fatigue Using Electromyography Signals

Review and comparison of linear algorithms to quantify muscle fatigue based on sEMG signals

Surface electromyography techniques are widely used in field of motion analysis and ergonomics combining precise muscular activation assessment with low-invasiveness and wearability. The aim of this investigation is to identify the myoelectrical manifestations of fatigue and to compare the effectiveness of sEMG-based quantitative indices for fatigue assessment. The investigated indexes are the ARV and RMS signal amplitudes, the mean frequency, the median frequency, the Dimitrov index, the instantaneous mean frequency and Wavelet distribution-based WIRE51 index. Two different protocols were developed, and the activity of the lateral deltoid and middle trapezius muscles was recorded. The WIRE51 index is found to have the highest sensitivity in the detection of the difference between the repetitions of each exercise for both protocols. Due to the lack of a unified standard for the performance comparison of fatigue indices, a correlation analysis was carried out between the result provided by the indices and the subjective fatigue perception employing the RPE scale.

Exploration and Application of a Muscle Fatigue Assessment Model Based on NMF for Multi-Muscle Synergistic Movements

Muscle fatigue significantly impacts coordination, stability, and speed in daily activities. Accurate assessment of muscle fatigue is vital for effective exercise programs, injury prevention, and sports performance enhancement. Current methods mostly focus on individual muscles and strength evaluation, overlooking overall fatigue in multi-muscle movements. This study introduces a comprehensive muscle fatigue model using non-negative matrix factorization (NMF) weighting. NMF is employed to analyze the duration multi-muscle weight coefficient matrix (DMWCM) during synergistic movements, and four electromyographic (EMG) signal features in time, frequency, and complexity domains are selected. Particle Swarm Optimization (PSO) optimizes feature weights. The DMWCM and weighted features combine to calculate the Comprehensive Muscle Fatigue Index (CMFI) for multi-muscle synergistic movements. Experimental results show that CMFI correlates with perceived exertion (RPE) and Speed Dynamic Score (SDS), confirming its accuracy and real-time tracking in assessing multi-muscle synergistic movements. This model offers a more comprehensive approach to muscle fatigue assessment, with potential benefits for exercise training, injury prevention, and sports medicine.

A new fractional fuzzy dispersion entropy and its application in muscle fatigue detection

Recently, fuzzy dispersion entropy (DispEn) has attracted much attention as a new nonlinear dynamics method that combines the advantages of both DispEn and fuzzy entropy. However, it suffers from limitation of insensitivity to dynamic changes. To solve this limitation, we proposed fractional fuzzy dispersion entropy (FFDispEn) based on DispEn, a novel fuzzy membership function and fractional calculus. The fuzzy membership function was defined based on the Euclidean distance between the embedding vector and dispersion pattern. Simulated signals generated by the one-dimensional (1D) logistic map were used to test the sensitivity of the proposed method to dynamic changes. Moreover, 29 subjects were recruited for an upper limb muscle fatigue experiment, during which surface electromyography (sEMG) signals of the biceps brachii muscle were recorded. Both simulated signals and sEMG signals were processed using a sliding window approach. Sample entropy (SampEn), DispEn and FFDispEn were separately used to calculate the complexity of each frame. The sensitivity of different algorithms to the muscle fatigue process was analyzed using fitting parameters through linear fitting of the complexity of each frame signal. The results showed that for simulated signals, the larger the fractional order q, the higher the sensitivity to dynamic changes. Moreover, DispEn performed poorly in the sensitivity to dynamic changes compared with FFDispEn. As for muscle fatigue detection, the FFDispEn value showed a clear declining tendency with a mean slope of -1.658 × 10-3 as muscle fatigue progresses; additionally, it was more sensitive to muscle fatigue compared with SampEn (slope: -0.4156 × 10-3) and DispEn (slope: -0.1675 × 10-3). The highest accuracy of 97.5% was achieved with the FFDispEn and support vector machine (SVM). This study provided a new useful nonlinear dynamic indicator for sEMG signal processing and muscle fatigue analysis. The proposed method may be useful for physiological and biomedical signal analysis.

Simplicial complex entropy for time series analysis

The complex behavior of many systems in nature requires the application of robust methodologies capable of identifying changes in their dynamics. In the case of time series (which are sensed values of a system during a time interval), several methods have been proposed to evaluate their irregularity. However, for some types of dynamics such as stochastic and chaotic, new approaches are required that can provide a better characterization of them. In this paper we present the simplicial complex approximate entropy, which is based on the conditional probability of the occurrence of elements of a simplicial complex. Our results show that this entropy measure provides a wide range of values with details not easily identifiable with standard methods. In particular, we show that our method is able to quantify the irregularity in simulated random sequences and those from low-dimensional chaotic dynamics. Furthermore, it is possible to consistently differentiate cardiac interbeat sequences from healthy subjects and from patients with heart failure, as well as to identify changes between dynamical states of coupled chaotic maps. Our results highlight the importance of the structures revealed by the simplicial complexes, which holds promise for applications of this approach in various contexts.

A study of EEG non-stationarity on inducing false memory in different emotional states

False memory leads to inaccurate decisions and unnecessary challenges. Researchers have conventionally used electroencephalography (EEG) to study false memory under different emotional states. However, EEG non-stationarity has scarcely been investigated. To address this problem, this study utilized the nonlinear method of recursive quantitative analysis to analyze the non-stationarity of EEG signals. Deese-Roediger-McDermott paradigm experiments were used to induce false memory wherein semantic words were highly correlated. The EEG signals of 48 participants with false memory associated with different emotional states were collected. Recurrence rate (RR), determination rate (DET), and entropy recurrence (ENTR) data were generated to characterize EEG non-stationarity. Behavioral outcomes exhibited significantly higher false-memory rates in the positive group than in the negative group. The prefrontal, temporal, and parietal regions yielded significantly higher RR, DET, and ENTR values than other brain regions in the positive group. However, only the prefrontal region had significantly higher values than other brain regions in the negative group. Therefore, positive emotions enhance non-stationarity in brain regions associated with semantics compared with negative emotions, leading to a higher false-memory rate. This suggests that non-stationary alterations in brain regions under different emotional states are correlated with false memory.

Influence of the Fuzzy Function on the Estimation of the Fuzzy Sample Entropy with Fixed Tolerance Values for the Evaluation of Surface EMG Muscle Activity

Fixed sample entropy (fSampEn) is a technique that has demonstrated superior performance to other amplitude estimators for assessing respiratory muscle electromyographic activity. This technique is based on the calculation of sample entropy (SampEn) using fixed tolerance thresholds. Fuzzy entropy (FuzzyEn) introduces an improvement to the SampEn algorithm based on the use of a fuzzy measure to evaluate the similarity between vectors. However, several fuzzy functions have been used to calculate the FuzzyEn, and not all of them allow an effective comparison with the SampEn calculation parameters. In the present work, an analysis of the different fuzzy functions previously used has been carried out and a new sigmoid fuzzy function for the calculation of FuzzyEn with fixed tolerance thresholds (fFuzzyEn) has been proposed. The results show that the proposed fuzzy function outperformed both fSampEn and previously proposed FuzzyEn-based algorithms. These results suggest that fFuzzyEn could improve the assessment of muscle activity providing potentially useful diagnostic information.Clinical Relevance- This sets out the appropriate use of the fuzzy function for the estimation of the fuzzy sample entropy with fixed tolerance thresholds (fFuzzyEn). The use of fFuzzyEn could improve methods for detecting the onset and offset of respiratory electromyographic (EMG) signals, as well as the assessment of EMG activation level.

The interaction between changes of muscle activation and cortical network dynamics during isometric elbow contraction: a sEMG and fNIRS study

Objective: The relationship between muscle activation during motor tasks and cerebral cortical activity remains poorly understood. The aim of this study was to investigate the correlation between brain network connectivity and the non-linear characteristics of muscle activation changes during different levels of isometric contractions. Methods: Twenty-one healthy subjects were recruited and were asked to perform isometric elbow contractions in both dominant and non-dominant sides. Blood oxygen concentrations in brain from functional Near-infrared Spectroscopy (fNIRS) and surface electromyography (sEMG) signals in the biceps brachii (BIC) and triceps brachii (TRI) muscles were recorded simultaneously and compared during 80% and 20% of maximum voluntary contraction (MVC). Functional connectivity, effective connectivity, and graph theory indicators were used to measure information interaction in brain activity during motor tasks. The non-linear characteristics of sEMG signals, fuzzy approximate entropy (fApEn), were used to evaluate the signal complexity changes in motor tasks. Pearson correlation analysis was used to examine the correlation between brain network characteristic values and sEMG parameters under different task conditions. Results: The effective connectivity between brain regions in motor tasks in dominant side was significantly higher than that in non-dominant side under different contractions (p < 0.05). The results of graph theory analysis showed that the clustering coefficient and node-local efficiency of the contralateral motor cortex were significantly varied under different contractions (p < 0.01). fApEn and co-contraction index (CCI) of sEMG under 80% MVC condition were significantly higher than that under 20% MVC condition (p < 0.05). There was a significant positive correlation between the fApEn and the blood oxygen value in the contralateral brain regions in both dominant or non-dominant sides (p < 0.001). The node-local efficiency of the contralateral motor cortex in the dominant side was positively correlated with the fApEn of the EMG signals (p < 0.05). Conclusion: In this study, the mapping relationship between brain network related indicators and non-linear characteristic of sEMG in different motor tasks was verified. These findings provide evidence for further exploration of the interaction between the brain activity and the execution of motor tasks, and the parameters might be useful in evaluation of rehabilitation intervention.

Shoulder electromyography-based indicators to assess manifestation of muscle fatigue during laboratory-simulated manual handling task

Muscle fatigue is a risk factor for developing shoulder musculoskeletal disorders. The aim of this study was to identify shoulder electromyographic indicators that are most indicative of muscle fatigue during a laboratory simulated manual handling task. Thirty-two participants were equipped with electromyographic electrodes on 10 shoulder muscles and moved boxes for 45-minutes. The modified rate of perceived exertion (mRPE) was assessed every 5-minutes and multivariate linear regressions were performed between myoelectric manifestation of fatigue (MMF) and the mRPE scores. During a manual handling task representative of industry working conditions, spectral entropy, median frequency, and mobility were the electromyographic indicators that explained the largest percentage of the mRPE. Overall, the deltoids, biceps and upper trapezius were the muscles that most often showed significant changes over time in their electromyographic indicators. The combination of these three indicators may improve the accuracy for the assessment of MMF during manual handling. Practitioner Summary: To date, muscle fatigue has primarily been assessed during tasks done to exhaustion, which are not representative of typical working conditions. During a manual handling task representative of industry working conditions, EMG-derived spectral entropy, and median frequency, both extracted from time-frequency analysis, and mobility extracted from time domain, were the best indicators of the manifestation of muscle fatigue.

A Weak Monotonicity Based Muscle Fatigue Detection Algorithm for a Short-Duration Poor Posture Using sEMG Measurements

Muscle fatigue is usually defined as a decrease in the ability to produce force. The surface electromyography (sEMG) signals have been widely used to provide information about muscle activities including detecting muscle fatigue by various data-driven techniques such as machine learning and statistical approaches. However, it is well-known that sEMGs are usually weak signals with a smaller amplitude and a lower signal-to-noise ratio, making it difficult to apply the traditional signal processing techniques. In particular, the existing methods cannot work well to detect muscle fatigue coming from static poses. This work exploits the concept of weak monotonicity, which has been observed in the process of fatigue, to robustly detect muscle fatigue in the presence of measurement noises and human variations. Such a population trend methodology has shown its potential in muscle fatigue detection as demonstrated by the experiment of a static pose.

Abnormal EEG Complexity and Alpha Oscillation of Resting State in Chronic Stroke Patients

A valid evaluation of neurological functions after stroke may improve clinical decision-making. The aim of this study was to compare the performance of EEG-related indexes in differentiating stroke patients from control participants, and to investigate pathological EEG changes after chronic stroke. 20 stroke and 13 healthy participants were recruited, and spontaneous EEG signals were recorded during the resting state. EEG rhythms and complexity were calculated based on Fast Fourier Transform and the fuzzy approximate entropy (fApEn) algorithm. The results showed a significant difference of alpha rhythm (p = 0.019) and fApEn (p = 0.003) of EEG signals from brain area among ipsilesional, contralesion hemisphere of stroke patients and corresponding brain hemisphere of healthy participants. EEG fApEn had the best classification accuracy (84.85%), sensitivity (85.00%), and specificity (84.62%) among these EEG-related indexes. Our study provides a potential method to evaluate alterations in the properties of the injured brain, which help us to understand neurological change in chronic strokes.

Removal of ECG Artifacts Affects Respiratory Muscle Fatigue Detection-A Simulation Study

This work investigates elimination methods for cardiogenic artifacts in respiratory surface electromyographic (sEMG) signals and compares their performance with respect to subsequent fatigue detection with different fatigue algorithms. The analysis is based on artificially constructed test signals featuring a clearly defined expected fatigue level. Test signals are additively constructed with different proportions from sEMG and electrocardiographic (ECG) signals. Cardiogenic artifacts are eliminated by high-pass filtering (HP), template subtraction (TS), a newly introduced two-step approach (TSWD) consisting of template subtraction and a wavelet-based damping step and a pure wavelet-based damping (DSO). Each method is additionally combined with the exclusion of QRS segments (gating). Fatigue is subsequently quantified with mean frequency (MNF), spectral moments ratio of order five (SMR5) and fuzzy approximate entropy (fApEn). Different combinations of artifact elimination methods and fatigue detection algorithms are tested with respect to their ability to deliver invariant results despite increasing ECG contamination. Both DSO and TSWD artifact elimination methods displayed promising results regarding the intermediate, "cleaned" EMG signal. However, only the TSWD method enabled superior results in the subsequent fatigue detection across different levels of artifact contamination and evaluation criteria. SMR5 could be determined as the best fatigue detection algorithm. This study proposes a signal processing chain to determine neuromuscular fatigue despite the presence of cardiogenic artifacts. The results furthermore underline the importance of selecting a combination of algorithms that play well together to remove cardiogenic artifacts and to detect fatigue. This investigation provides guidance for clinical studies to select optimal signal processing to detect fatigue from respiratory sEMG signals.

Effects of Muscle Fatigue and Recovery on Complexity of Surface Electromyography of Biceps Brachii

This study aimed to investigate the degree of regularity of surface electromyography (sEMG) signals during muscle fatigue during dynamic contractions and muscle recovery after cupping therapy. To the best of our knowledge, this is the first study assessing both muscle fatigue and muscle recovery using a nonlinear method. Twelve healthy participants were recruited to perform biceps curls at 75% of the 10 repetitions maximum under four conditions: immediately and 24 h after cupping therapy (-300 mmHg pressure), as well as after sham control (no negative pressure). Cupping therapy or sham control was assigned to each participant according to a pre-determined counter-balanced order and applied to the participant's biceps brachii for 5 min. The degree of regularity of the sEMG signal during the first, second, and last 10 repetitions (Reps) of biceps curls was quantified using a modified sample entropy (E_ms) algorithm. When exercise was performed immediately or 24 h after sham control, E_ms of the sEMG signal showed a significant decrease from the first to second 10 Reps; when exercise was performed immediately after cupping therapy, E_ms also showed a significant decrease from the first to second 10 Reps but its relative change was significantly smaller compared to the condition of exercise immediately after sham control. When exercise was performed 24 h after cupping therapy, E_ms did not show a significant decrease, while its relative change was significantly smaller compared to the condition of exercise 24 h after sham control. These results indicated that the degree of regularity of sEMG signals quantified by E_ms is capable of assessing muscle fatigue and the effect of cupping therapy. Moreover, this measure seems to be more sensitive to muscle fatigue and could yield more consistent results compared to the traditional linear measures.

Revisiting the lumbosacral orthosis from the perspective of dynamical systems theory: a preliminary randomized clinical trial on patients with chronic low back pain

BACKGROUND

The prevalent method for investigating the effect of therapeutic interventions on walking in the individuals with chronic low back pain (CLBP) is component-level approach in which all measurements focus on the spine component alone. However, this approach cannot disclose information about the overall function of the movement system such as complex walking patterns, which, in turn, reveal the underlying movement control.

OBJECTIVES

To compare the effect of 3-week wearing of lumbosacral orthosis (LSO) along with routine physical therapy with routine physical therapy alone on walking complexity in the individuals with nonspecific CLBP on the basis of the systems approach.

STUDY DESIGN

Preliminary randomized clinical trial.

METHODS

Twenty-four subjects were randomly allocated to two groups. The control group received the routine physical therapy for 3 weeks. The intervention group received the same program plus an LSO. Nonlinear analysis was used to quantify walking complexity, as behavior of the entire movement system, before and after the intervention and at 1-month follow-up.

RESULTS

An average of 496 strides during ten minutes of walking was used for analysis. There was no significant difference (p > 0.05) in degree of walking complexity between two groups during all evaluation periods.

CONCLUSIONS

The administered orthotic intervention did not alter walking complexity. This suggests that therapeutic goal of current LSOs, which is not based on the systems approach, cannot recover the emergent behavior of the movement system. This may be a potential source of controversies.

CLINICAL RELEVANCE

To achieve an effective treatment, orthotists should focus on the individuals themselves, not only on their CLBP symptoms. Although the component-level approach aims to decrease the symptoms, the systems approach focuses on the whole context that fosters LBP symptoms.

Effect of BoNT/A in the Surface Electromyographic Characteristics of the Pelvic Floor Muscles for the Treatment of Chronic Pelvic Pain

Chronic pelvic pain (CPP) is a complex condition with a high economic and social burden. Although it is usually treated with botulinum neurotoxin type A (BoNT/A) injected into the pelvic floor muscles (PFM), its effect on their electrophysiological condition is unknown. In this study, 24 CPP patients were treated with BoNT/A. Surface electromyographic signals (sEMG) were recorded at Weeks 0 (infiltration), 8, 12 and 24 from the infiltrated, non-infiltrated, upper and lower PFM. The sEMG of 24 healthy women was also recorded for comparison. Four parameters were computed: root mean square (RMS), median frequency (MDF), Dimitrov’s index (DI) and sample entropy (SampEn). An index of pelvic electrophysiological impairment (IPEI) was also defined with respect to the healthy condition. Before treatment, the CPP and healthy parameters of almost all PFM sides were significantly different. Post-treatment, there was a significant reduction in power (<RMS), a shift towards higher frequencies (>MDF), lower fatigue index (<DI) and increased information complexity (>SampEn) in all sites in patients, mainly during PFM contractions, which brought their electrophysiological condition closer to that of healthy women (<IPEI). sEMG can be used to assess the PFM electrophysiological condition of CPP patients and the effects of therapies such as BoNT/A infiltration.

Bivariate Entropy Analysis of Electrocardiographic RR-QT Time Series

QT interval variability (QTV) and heart rate variability (HRV) are both accepted biomarkers for cardiovascular events. QTV characterizes the variations in ventricular depolarization and repolarization. It is a predominant element of HRV. However, QTV is also believed to accept direct inputs from upstream control system. How QTV varies along with HRV is yet to be elucidated. We studied the dynamic relationship of QTV and HRV during different physiological conditions from resting, to cycling, and to recovering. We applied several entropy-based measures to examine their bivariate relationships, including cross sample entropy (XSampEn), cross fuzzy entropy (XFuzzyEn), cross conditional entropy (XCE), and joint distribution entropy (JDistEn). Results showed no statistically significant differences in XSampEn, XFuzzyEn, and XCE across different physiological states. Interestingly, JDistEn demonstrated significant decreases during cycling as compared with that during the resting state. Besides, JDistEn also showed a progressively recovering trend from cycling to the first 3 min during recovering, and further to the second 3 min during recovering. It appeared to be fully recovered to its level in the resting state during the second 3 min during the recovering phase. The results suggest that there is certain nonlinear temporal relationship between QTV and HRV, and that the JDistEn could help unravel this nuanced property.

Muscle Fatigue Analysis With Optimized Complementary Ensemble Empirical Mode Decomposition and Multi-Scale Envelope Spectral Entropy

The preprocessing of surface electromyography (sEMG) signals with complementary ensemble empirical mode decomposition (CEEMD) improves frequency identification precision and temporal resolution, and lays a good foundation for feature extraction. However, a mode-mixing problem often occurs when the CEEMD decomposes an sEMG signal that exhibits intermittency and contains components with a near-by spectrum into intrinsic mode functions (IMFs). This paper presents a method called optimized CEEMD (OCEEMD) to solve this problem. The method integrates the least-squares mutual information (LSMI) and the chaotic quantum particle swarm optimization (CQPSO) algorithm in signal decomposition. It uses the LSMI to calculate the correlation between IMFs so as to reduce mode mixing and uses the CQPSO to optimize the standard deviation of Gaussian white noise so as to improve iteration efficiency. Then, useful IMFs are selected and added to reconstruct a de-noised signal. Finally, considering that the IMFs contain abundant frequency and envelope information, this paper extracts the multi-scale envelope spectral entropy (MSESEn) from the reconstructed sEMG signal. Some original sEMG signals, which were collected from experiments, were used to validate the methods. Compared with the CEEMD and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the OCEEMD effectively suppresses mode mixing between IMFs with rapid iteration. Compared with approximate entropy (ApEn) and sample entropy (SampEn), the MSESEn clearly shows a declining tendency with time and is sensitive to muscle fatigue. This suggests a potential use of this approach for sEMG signal preprocessing and the analysis of muscle fatigue.

Complexity Analysis of Surface Electromyography for Assessing the Myoelectric Manifestation of Muscle Fatigue: A Review

The surface electromyography (sEMG) records the electrical activity of muscle fibers during contraction: one of its uses is to assess changes taking place within muscles in the course of a fatiguing contraction to provide insights into our understanding of muscle fatigue in training protocols and rehabilitation medicine. Until recently, these myoelectric manifestations of muscle fatigue (MMF) have been assessed essentially by linear sEMG analyses. However, sEMG shows a complex behavior, due to many concurrent factors. Therefore, in the last years, complexity-based methods have been tentatively applied to the sEMG signal to better individuate the MMF onset during sustained contractions. In this review, after describing concisely the traditional linear methods employed to assess MMF we present the complexity methods used for sEMG analysis based on an extensive literature search. We show that some of these indices, like those derived from recurrence plots, from entropy or fractal analysis, can detect MMF efficiently. However, we also show that more work remains to be done to compare the complexity indices in terms of reliability and sensibility; to optimize the choice of embedding dimension, time delay and threshold distance in reconstructing the phase space; and to elucidate the relationship between complexity estimators and the physiologic phenomena underlying the onset of MMF in exercising muscles.

Upper and lower limb performance fatigability in people with multiple sclerosis investigated through surface electromyography: a pilot study

OBJECTIVE

Fatigue experienced by people with multiple sclerosis (pwMS) is multidimensional, consisting of different components, such as perceived, physical and cognitive fatigue and performance fatigability. At present, there is no gold standard to assess performance fatigability in pwMS; therefore, we aimed to determine whether, during a fatiguing task, average rectified value (ARV), mean frequency of the power spectrum (MNF), muscle fiber conduction velocity (CV) and fractal dimension (FD) of surface electromyography (sEMG) may be used as indirect indices of performance fatigability. Moreover, we analyzed whether a three-week rehabilitation program impacts on performance fatigability in pwMS, and whether a relationship between sEMG parameters and trait levels of perceived fatigability, before and after rehabilitation, does exist.

APPROACH

Twenty-one pwMS performed a 20% maximal voluntary contraction (MVC) of 1 min, and afterwards a 60% MVC held until exhaustion. sEMG signals were detected from the biceps brachii, vastus medialis and vastus lateralis. Performance fatigability was determined at entry to (t ₀) and discharge from (t ₁) rehabilitation. Perceived fatigability was measured at t ₀ and t ₂, one month after rehabilitation.

MAIN RESULTS

ARV, MNF, CV and FD rates of change showed significant changes at t ₀ and t ₁ (p  <  0.05) during the high-level contraction in the BB, but rather limited in the vastii muscles. Moreover, rehabilitation did not induce any reductions in either perceived or performance fatigability. No significant correlations between ARV, MNF, CV and FD rates of change during the 60% MVC and perceived fatigability, at t ₀ and t ₂, were found.

SIGNIFICANCE

Our findings suggest that the sEMG parameters are useful for indirectly assessing performance fatigability in pwMS during sub-maximal fatiguing contractions, particularly in the biceps brachii.

Detection of stretch reflex onset based on empirical mode decomposition and modified sample entropy

Background

Accurate spasticity assessment provides an objective evaluation index for the rehabilitation treatment of patients with spasticity, and the key is detecting stretch reflex onset. The surface electromyogram of patients with spasticity is prone to false peaks, and its data length is unstable. These conditions decrease signal differences before and after stretch reflex onset. Therefore, a method for detecting stretch reflex onset based on empirical mode decomposition denoising and modified sample entropy recognition is proposed in this study.

Results

The empirical mode decomposition algorithm is better than the wavelet threshold algorithm in denoising surface electromyogram signal. Without adding Gaussian white noise to the electromyogram signal, the stretch reflex onset recognition rate of the electromyogram signal before and after empirical mode decomposition denoising was increased by 56%. In particular, the recognition rate of stretch reflex onset under the optimal parameter of the modified sample entropy can reach up to 100% and the average recognition rate is 93%.

Conclusions

The empirical mode decomposition algorithm can eliminate the baseline activity of the surface electromyogram signal before stretch reflex onset and effectively remove noise from the signal. The identification of stretch reflex onset using combined empirical mode decomposition and modified sample entropy is better than that via modified sample entropy alone, and stretch reflex onset can be accurately determined.

An EMG Patch for the Real-Time Monitoring of Muscle-Fatigue Conditions During Exercise

In recent years, wearable monitoring devices have been very popular in the health care field and are being used to avoid sport injuries during exercise. They are usually worn on the wrist, the same as sport watches, or on the chest, like an electrocardiogram patch. Common functions of these wearable devices are that they use real time to display the state of health of the body, and they are all small sized. The electromyogram (EMG) signal is usually used to show muscle activity. Thus, the EMG signal could be used to determine the muscle-fatigue conditions. In this study, the goal is to develop an EMG patch which could be worn on the lower leg, the gastrocnemius muscle, to detect real-time muscle fatigue while exercising. A micro controller unit (MCU) in the EMG patch is part of an ARM Cortex-M4 processor, which is used to measure the median frequency (MF) of an EMG signal in real time. When the muscle starts showing tiredness, the median frequency will shift to a low frequency. In order to delete the noise of the isotonic EMG signal, the EMG patch has to run the empirical mode decomposition algorithm. A two-electrode circuit was designed to measure the EMG signal. The maximum power consumption of the EMG patch was about 39.5 mAh. In order to verify that the real-time MF values measured by the EMG patch were close to the off-line MF values measured by the computer system, we used the root-mean-square value to estimate the difference in the real-time MF values and the off-line MF values. There were 20 participants that rode an exercise bicycle at different speeds. Their EMG signals were recorded with an EMG patch and a physiological measurement system at the same time. Every participant rode the exercise bicycle twice. The averaged root-mean-square values were 2.86 ± 0.86 Hz and 2.56 ± 0.47 Hz for the first and second time, respectively. Moreover, we also developed an application program implemented on a smart phone to display the participants' muscle-fatigue conditions and information while exercising. Therefore, the EMG patch designed in this study could monitor the muscle-fatigue conditions to avoid sport injuries while exercising.

Motion During Acquisition is Associated With fMRI Brain Entropy

Measures of fMRI brain entropy have been used to investigate age and disease related neural changes. However, it is unclear if movement in the scanner is associated with brain entropy after geometric correction for movement. As age and disease can affect motor control, quantifying and correcting for the influence of movement will avoid false findings. This paper examines the influence of head motion on fMRI brain entropy. Resting-state and task-based fMRI data from 281 individuals born in Aberdeen between 1950 and 1956 were analyzed. The images were realigned, followed by nuisance regression of the head motion parameters. The images were either high-pass filtered (0.008 Hz) or band-pass (0.008-0.1 Hz) filtered in order to compare the two methods; fuzzy approximate entropy and fuzzy sample entropy were calculated for every voxel. Motion was quantified as the mean displacement and mean rotation in three dimensions. Greater mean motion was correlated with decreased entropy for all four methods of calculating entropy. Different movement characteristics produce different patterns of associations, which appear to be artefact. However, across all motion metrics, entropy calculation methods, and scan conditions, a number of regions consistently show a significant negative association: the right cerebellar crus, left precentral gyrus (primary motor cortex), the left postcentral gyrus (primary somatosensory cortex), and the opercular part of the left inferior frontal gyrus. The robustness of our findings at these locations suggests that decreased entropy in specific brain regions may be a marker for decreased motor control.

Spatial complexity of brain signal is altered in patients with generalized anxiety disorder

BACKGROUND

Is it healthy to be chaotic? Recent studies have argued that mental disorders are associated with more orderly neural activities, corresponding to a less flexible functional system. These conclusions were derived from altered temporal complexity. However, the relationship between spatial complexity and health is unknown, although spatial configuration is of importance for normal brain function.

METHODS

Based on resting-state functional magnetic resonance imaging data, we used Sample entropy (SampEn) to evaluate the altered spatial complexity in patients with generalized anxiety disorder (GAD; n = 47) compared to healthy controls (HCs; n = 38) and the relationship between spatial complexity and anxiety level.

RESULTS

Converging results showed increased spatial complexity in patients with GAD, indicating more chaotic spatial configuration. Interestingly, inverted-U relationship was revealed between spatial complexity and anxiety level, suggesting complex relationship between health and the chaos of human brain.

LIMITATIONS

Anxiety-related alteration of spatial complexity should be verified at voxel level in a larger sample and compared with results of other indices to clarify the mechanism of spatial chaotic of anxiety.

CONCLUSIONS

Altered spatial complexity in the brain of GAD patients mirrors inverted-U relationship between anxiety and behavioral performance, which may reflect an important characteristic of anxiety. These results indicate that SampEn is a good reflection of human health or trait mental characteristic.

Driver drowsiness detection based on classification of surface electromyography features in a driving simulator

Driver drowsiness is a significant cause of fatal crashes every year in the world. In this research, driver's drowsiness is detected by classifying surface electromyography signal features. The tests are conducted on 13 healthy subjects in a driving simulator with a monotonous route. The surface electromyography signal from the upper arm and shoulder muscles are measured including mid deltoid, clavicular portion of the pectoralis major, and triceps and biceps long heads. Signals are separated into 30-s epochs. Five features including range, variance, relative spectral power, kurtosis, and shape factor are extracted. The Observer Rating of Drowsiness evaluates the level of drowsiness. A binormal function is fitted for each feature. For classification, six classifiers are applied. The results show that the k-nearest neighbor classifier predicts drowsiness by 90% accuracy, 82% precision, 77% sensitivity, and 92% specificity.

Multidimensional and fuzzy sample entropy (SampEnMF) for quantifying H&E histological images of colorectal cancer

In this study, we propose to use a method based on the combination of sample entropy with multiscale and multidimensional approaches, along with a fuzzy function. The model was applied to quantify and classify H&E histological images of colorectal cancer. The multiscale approach was defined by analysing windows of different sizes and variations in tolerance for determining pattern similarity. The multidimensional strategy was performed by considering each pixel in the colour image as an n-dimensional vector, which was analysed from the Minkowski distance. The fuzzy strategy was a Gaussian function used to verify the pertinence of the distances between windows. The result was a method capable of computing similarities between pixels contained in windows of various sizes, as well as the information present in the colour channels. The power of quantification was tested in a public colorectal image dataset, which was composed of both benign and malignant classes. The results were given as inputs for classifiers of different categories and analysed by applying the k-fold cross-validation and holdout methods. The derived performances indicate that the proposed association was capable of distinguishing the benign and malignant groups, with values that surpassed those results obtained with important techniques available in the Literature. The best performance was an AUC value of 0.983, an important result, mainly when we consider the difficulties of clinical practice for the diagnosis of the colorectal cancer.

Characterization of the Stroke-Induced Changes in the Variability and Complexity of Handgrip Force

Introduction: The variability and complexity of handgrip forces in various modulations were investigated to identify post-stroke changes in force modulation, and extend our understanding of stroke-induced deficits. Methods: Eleven post-stroke subjects and ten age-matched controls performed voluntary grip force control tasks (power-grip tasks) at three contraction levels, and stationary dynamometer holding tasks (stationary holding tasks). Variability and complexity were described with root mean square jerk (RMS-jerk) and fuzzy approximate entropy (fApEn), respectively. Force magnitude, Fugl-Meyer upper extremity assessment and Wolf motor function test were also evaluated. Results: Comparing the affected side with the controls, fApEn was significantly decreased and RMS-jerk increased across the three levels in power-grip tasks, and fApEn was significantly decreased in stationary holding tasks. There were significant strong correlations between RMS-jerk and clinical scales in power-grip tasks. Discussion: Abnormal neuromuscular control, altered mechanical properties, and atrophic motoneurons could be the main causes of the differences in complexity and variability in post-stroke subjects.

Occupational functional plasticity revealed by brain entropy: A resting-state fMRI study of seafarers

Recently, functional magnetic resonance imaging (fMRI) has been increasingly used to assess brain function. Brain entropy is an effective model for evaluating the alteration of brain complexity. Specifically, the sample entropy (SampEn) provides a feasible solution for revealing the brain's complexity. Occupation is one key factor affecting the brain's activity, but the neuropsychological mechanisms are still unclear. Thus, in this article, based on fMRI and a brain entropy model, we explored the functional complexity changes engendered by occupation factors, taking the seafarer as an example. The whole-brain entropy values of two groups (i.e., the seafarers and the nonseafarers) were first calculated by SampEn and followed by a two-sample t test with AlphaSim correction (p < .05). We found that the entropy of the orbital-frontal gyrus (OFG) and superior temporal gyrus (STG) in the seafarers was significantly higher than that of the nonseafarers. In addition, the entropy of the cerebellum in the seafarers was lower than that of the nonseafarers. We conclude that (1) the lower entropy in the cerebellum implies that the seafarers' cerebellum activity had strong regularity and consistency, suggesting that the seafarer's cerebellum was possibly more specialized by the long-term career training; (2) the higher entropy in the OFG and STG possibly demonstrated that the seafarers had a relatively decreased capability for emotion control and auditory information processing. The above results imply that the seafarer occupation indeed impacted the brain's complexity, and also provided new neuropsychological evidence of functional plasticity related to one's career.

Recovery of facial expressions using functional electrical stimulation after full-face transplantation

BACKGROUND

We assessed the recovery of 2 face transplantation patients with measures of complexity during neuromuscular rehabilitation. Cognitive rehabilitation methods and functional electrical stimulation were used to improve facial emotional expressions of full-face transplantation patients for 5 months. Rehabilitation and analyses were conducted at approximately 3 years after full facial transplantation in the patient group. We report complexity analysis of surface electromyography signals of these two patients in comparison to the results of 10 healthy individuals.

METHODS

Facial surface electromyography data were collected during 6 basic emotional expressions and 4 primary facial movements from 2 full-face transplantation patients and 10 healthy individuals to determine a strategy of functional electrical stimulation and understand the mechanisms of rehabilitation. A new personalized rehabilitation technique was developed using the wavelet packet method. Rehabilitation sessions were applied twice a month for 5 months. Subsequently, motor and functional progress was assessed by comparing the fuzzy entropy of surface electromyography data against the results obtained from patients before rehabilitation and the mean results obtained from 10 healthy subjects.

RESULTS

At the end of personalized rehabilitation, the patient group showed improvements in their facial symmetry and their ability to perform basic facial expressions and primary facial movements. Similarity in the pattern of fuzzy entropy for facial expressions between the patient group and healthy individuals increased. Synkinesis was detected during primary facial movements in the patient group, and one patient showed synkinesis during the happiness expression. Synkinesis in the lower face region of one of the patients was eliminated for the lid tightening movement.

CONCLUSIONS

The recovery of emotional expressions after personalized rehabilitation was satisfactory to the patients. The assessment with complexity analysis of sEMG data can be used for developing new neurorehabilitation techniques and detecting synkinesis after full-face transplantation.

Kinematic Outcome Measures using Target-Reaching Arm Movement in Stroke

This study aimed to quantitatively investigate upper extremity motor performance and disclose the abnormality of motor control induced by stroke. Ten patients and ten healthy subjects were instructed to perform target-reaching tasks at nine difficulty levels, and coordinates of the shoulder, elbow and tip of the index finger were recorded. Age-matched control performed significantly better than patients, as indicated by lower movement time (MT) and normalized jerk score (NJS) and higher peak velocity (V _peak), percentage time to peak velocity (PTPV), fuzzy approximate entropy (fApEn) and relative joint angles correlation (RJAC); also, significant effects of difficulty on all parameters except RJAC and fApEn, were observed in two groups. There were significant correlations between PTPV and Fugl-Meyer assessment for upper extremity (FMA-UE) and between RJAC and FMA-UE at certain difficulty levels. The stroke-related differences could be explained by the increase in intrinsic neuromotor noise, and the difficulty-related differences may be related to extrinsic neuromotor noise. The increase in either noises could result in a degradation in motor control. The significant linear relationships between some kinematic parameters and the clinical score suggested that the kinematic parameters could be applied as quantitative outcome measures in the clinic in the future.

Multiparameter Electromyography Analysis of the Masticatory Muscle Activities in Patients with Brainstem Stroke at Different Head Positions

The performance of the masticatory muscle is frequently affected and presents high heterogeneity poststroke. Surface electromyography (EMG) is widely used to quantify muscle movement patterns. However, only a few studies applied EMG analysis on the research of masticatory muscle activities poststroke, and most of which used single parameter—root mean squares (RMS). The aim of this study was to fully investigate the performance of masticatory muscle at different head positions in healthy subjects and brainstem stroke patients with multiparameter EMG analysis. In this study, 15 healthy subjects and six brainstem stroke patients were recruited to conduct maximum voluntary clenching at five different head positions: upright position, left rotation, right rotation, dorsal flexion, and ventral flexion. The EMG signals of bilateral temporalis anterior and masseter muscles were recorded, and parameters including RMS, median frequency, and fuzzy approximate entropy of the EMG signals were calculated. Two-way analysis of variance (ANOVA) with repeated measures and Bonferroni post hoc test were used to evaluate the effects of muscle and head position on EMG parameters in the healthy group, and the non-parametric Wilcoxon signed rank test was conducted in the patient group. The Welch–Satterthwaite t-test was used to compare the between-subject difference. We found a significant effect of subject and muscles but no significant effect of head positions, and the masticatory muscles of patients after brainstem stroke performed significantly different from healthy subjects. Multiparameter EMG analysis might be an informative tool to investigate the neural activity related movement patterns of the deficient masticatory muscles poststroke.

Entropy measures, entropy estimators, and their performance in quantifying complex dynamics: Effects of artifacts, nonstationarity, and long-range correlations

Entropy measures are widely applied to quantify the complexity of dynamical systems in diverse fields. However, the practical application of entropy methods is challenging, due to the variety of entropy measures and estimators and the complexity of real-world time series, including nonstationarities and long-range correlations (LRC). We conduct a systematic study on the performance, bias, and limitations of three basic measures (entropy, conditional entropy, information storage) and three traditionally used estimators (linear, kernel, nearest neighbor). We investigate the dependence of entropy measures on estimator- and process-specific parameters, and we show the effects of three types of nonstationarities due to artifacts (trends, spikes, local variance change) in simulations of stochastic autoregressive processes. We also analyze the impact of LRC on the theoretical and estimated values of entropy measures. Finally, we apply entropy methods on heart rate variability data from subjects in different physiological states and clinical conditions. We find that entropy measures can only differentiate changes of specific types in cardiac dynamics and that appropriate preprocessing is vital for correct estimation and interpretation. Demonstrating the limitations of entropy methods and shedding light on how to mitigate bias and provide correct interpretations of results, this work can serve as a comprehensive reference for the application of entropy methods and the evaluation of existing studies.

Muscle Fatigue Analysis of the Deltoid during Three Head-Related Static Isometric Contraction Tasks

This study aimed to investigate the fatiguing characteristics of muscle-tendon units (MTUs) within skeletal muscles during static isometric contraction tasks. The deltoid was selected as the target muscle and three head-related static isometric contraction tasks were designed to activate three heads of the deltoid in different modes. Nine male subjects participated in this study. Surface electromyography (SEMG) signals were collected synchronously from the three heads of the deltoid. The performances of five SEMG parameters, including root mean square (RMS), mean power frequency (MPF), the first coefficient of autoregressive model (ARC1), sample entropy (SE) and Higuchi’s fractal dimension (HFD), in quantification of fatigue, were evaluated in terms of sensitivity to variability ratio (SVR) and consistency firstly. Then, the HFD parameter was selected as the fatigue index for further muscle fatigue analysis. The experimental results demonstrated that the three deltoid heads presented different activation modes during three head-related fatiguing contractions. The fatiguing characteristics of the three heads were found to be task-dependent, and the heads kept in a relatively high activation level were more prone to fatigue. In addition, the differences in fatiguing rate between heads increased with the increase in load. The findings of this study can be helpful in better understanding the underlying neuromuscular control strategies of the central nervous system (CNS). Based on the results of this study, the CNS was thought to control the contraction of the deltoid by taking the three heads as functional units, but a certain synergy among heads might also exist to accomplish a contraction task.

Effects of Force Load, Muscle Fatigue, and Magnetic Stimulation on Surface Electromyography during Side Arm Lateral Raise Task: A Preliminary Study with Healthy Subjects

The aim of this study was to quantitatively investigate the effects of force load, muscle fatigue, and extremely low-frequency (ELF) magnetic stimulation on surface electromyography (SEMG) signal features during side arm lateral raise task. SEMG signals were recorded from 18 healthy subjects on the anterior deltoid using a BIOSEMI ActiveTwo system during side lateral raise task (with the right arm 90 degrees away from the body) with three different loads on the forearm (0 kg, 1 kg, and 3 kg; their order was randomized between subjects). The arm maintained the loads until the subject felt exhausted. The first 10 s recording for each load was regarded as nonfatigue status and the last 10 s before the subject was exhausted was regarded as fatigue status. The subject was then given a five-minute resting between different loads. Two days later, the same experiment was repeated on every subject, and this time the ELF magnetic stimulation was applied to the subject's deltoid muscle during the five-minute rest period. Three commonly used SEMG features, root mean square (RMS), median frequency (MDF), and sample entropy (SampEn), were analyzed and compared between different loads, nonfatigue/fatigue status, and ELF stimulation and no stimulation. Variance analysis results showed that the effect of force load on RMS was significant (p < 0.001) but not for MDF and SampEn (both p > 0.05). In comparison with nonfatigue status, for all the different force loads with and without ELF stimulation, RMS was significantly larger at fatigue (all p < 0.001) and MDF and SampEn were significantly smaller (all p < 0.001).

Comparison of algorithms to quantify muscle fatigue in upper limb muscles based on sEMG signals

This work compared the performance of six different fatigue detection algorithms quantifying muscle fatigue based on electromyographic signals. Surface electromyography (sEMG) was obtained by an experiment from upper arm contractions at three different load levels from twelve volunteers. Fatigue detection algorithms mean frequency (MNF), spectral moments ratio (SMR), the wavelet method WIRM1551, sample entropy (SampEn), fuzzy approximate entropy (fApEn) and recurrence quantification analysis (RQA%DET) were calculated. The resulting fatigue signals were compared considering the disturbances incorporated in fatiguing situations as well as according to the possibility to differentiate the load levels based on the fatigue signals. Furthermore we investigated the influence of the electrode locations on the fatigue detection quality and whether an optimized channel set is reasonable. The results of the MNF, SMR, WIRM1551 and fApEn algorithms fell close together. Due to the small amount of subjects in this study significant differences could not be found. In terms of disturbances the SMR algorithm showed a slight tendency to out-perform the others.

Difficulty-dependent trajectory planning during target-reaching movements

This study explored how the difficulty of a task influenced motor control during target-reaching movements. During the experiment, twelve healthy subjects were recruited to perform target-reaching tasks with three different target sizes over three distances as quickly and accurately as possible using their index fingers. There were nine levels of difficulty of the tasks, with a combination of three target sizes and three distances, and the difficulty of the tasks could be measured by Fitts' law in terms of the index of difficulty (ID). The kinematic variables to represent movement performance were peak velocity (Vpeak), percentage time to peak velocity (PTPV), normalized jerk score (NJS) and fApEn (fuzzy approximate entropy). The results showed both distance and target size significantly influenced these parameters with the exception of the effect of the target size on Vpeak. Vpeak and fApEn were only linearly related to the ID when the individual target size across movement distances was considered. And a linear relationship between PTPV or NJS and ID was found. The increase in the difficulty of the task could lead to a shift from feedforward to feedback control by the central nerve system. The findings in this study contribute to an understanding of the underlying motor control during target reaching movements and can be applied as a quantitative method of evaluation in the clinic in the future.

Electromyographic Patterns during Golf Swing: Activation Sequence Profiling and Prediction of Shot Effectiveness

This study analyzes muscle activity, recorded in an eight-channel electromyographic (EMG) signal stream, during the golf swing using a 7-iron club and exploits information extracted from EMG dynamics to predict the success of the resulting shot. Muscles of the arm and shoulder on both the left and right sides, namely flexor carpi radialis, extensor digitorum communis, rhomboideus and trapezius, are considered for 15 golf players (∼5 shots each). The method using Gaussian filtering is outlined for EMG onset time estimation in each channel and activation sequence profiling. Shots of each player revealed a persistent pattern of muscle activation. Profiles were plotted and insights with respect to player effectiveness were provided. Inspection of EMG dynamics revealed a pair of highest peaks in each channel as the hallmark of golf swing, and a custom application of peak detection for automatic extraction of swing segment was introduced. Various EMG features, encompassing 22 feature sets, were constructed. Feature sets were used individually and also in decision-level fusion for the prediction of shot effectiveness. The prediction of the target attribute, such as club head speed or ball carry distance, was investigated using random forest as the learner in detection and regression tasks. Detection evaluates the personal effectiveness of a shot with respect to the player-specific average, whereas regression estimates the value of target attribute, using EMG features as predictors. Fusion after decision optimization provided the best results: the equal error rate in detection was 24.3% for the speed and 31.7% for the distance; the mean absolute percentage error in regression was 3.2% for the speed and 6.4% for the distance. Proposed EMG feature sets were found to be useful, especially when used in combination. Rankings of feature sets indicated statistics for muscle activity in both the left and right body sides, correlation-based analysis of EMG dynamics and features derived from the properties of two highest peaks as important predictors of personal shot effectiveness. Activation sequence profiles helped in analyzing muscle orchestration during golf shot, exposing a specific avalanche pattern, but data from more players are needed for stronger conclusions. Results demonstrate that information arising from an EMG signal stream is useful for predicting golf shot success, in terms of club head speed and ball carry distance, with acceptable accuracy. Surface EMG data, collected with a goal to automatically evaluate golf player's performance, enables wearable computing in the field of ambient intelligence and has potential to enhance exercising of a long carry distance drive.

Exploiting Complexity Information for Brain Activation Detection

We present a complexity-based approach for the analysis of fMRI time series, in which sample entropy (SampEn) is introduced as a quantification of the voxel complexity. Under this hypothesis the voxel complexity could be modulated in pertinent cognitive tasks, and it changes through experimental paradigms. We calculate the complexity of sequential fMRI data for each voxel in two distinct experimental paradigms and use a nonparametric statistical strategy, the Wilcoxon signed rank test, to evaluate the difference in complexity between them. The results are compared with the well known general linear model based Statistical Parametric Mapping package (SPM12), where a decided difference has been observed. This is because SampEn method detects brain complexity changes in two experiments of different conditions and the data-driven method SampEn evaluates just the complexity of specific sequential fMRI data. Also, the larger and smaller SampEn values correspond to different meanings, and the neutral-blank design produces higher predictability than threat-neutral. Complexity information can be considered as a complementary method to the existing fMRI analysis strategies, and it may help improving the understanding of human brain functions from a different perspective.

Variations in task constraints shape emergent performance outcomes and complexity levels in balancing

This study investigated the extent to which specific interacting constraints of performance might increase or decrease the emergent complexity in a movement system, and whether this could affect the relationship between observed movement variability and the central nervous system's capacity to adapt to perturbations during balancing. Fifty-two healthy volunteers performed eight trials where different performance constraints were manipulated: task difficulty (three levels) and visual biofeedback conditions (with and without the center of pressure (COP) displacement and a target displayed). Balance performance was assessed using COP-based measures: mean velocity magnitude (MVM) and bivariate variable error (BVE). To assess the complexity of COP, fuzzy entropy (FE) and detrended fluctuation analysis (DFA) were computed. ANOVAs showed that MVM and BVE increased when task difficulty increased. During biofeedback conditions, individuals showed higher MVM but lower BVE at the easiest level of task difficulty. Overall, higher FE and lower DFA values were observed when biofeedback was available. On the other hand, FE reduced and DFA increased as difficulty level increased, in the presence of biofeedback. However, when biofeedback was not available, the opposite trend in FE and DFA values was observed. Regardless of changes to task constraints and the variable investigated, balance performance was positively related to complexity in every condition. Data revealed how specificity of task constraints can result in an increase or decrease in complexity emerging in a neurobiological system during balance performance.

Fuzzy approximate entropy analysis of resting state fMRI signal complexity across the adult life span

In this study, we present a method for measuring functional magnetic resonance imaging (fMRI) signal complexity using fuzzy approximate entropy (fApEn) and compare it with the established sample entropy (SampEn). Here we use resting state fMRI dataset of 86 healthy adults (41 males) with age ranging from 19 to 85 years. We expect the complexity of the resting state fMRI signals measured to be consistent with the Goldberger/Lipsitz model for robustness where healthier (younger) and more robust systems exhibit more complexity in their physiological output and system complexity decrease with age. The mean whole brain fApEn demonstrated significant negative correlation (r = -0.472, p<0.001) with age. In comparison, SampEn produced a non-significant negative correlation (r = -0.099, p = 0.367). fApEn also demonstrated a significant (p < 0.05) negative correlation with age regionally (frontal, parietal, limbic, temporal and cerebellum parietal lobes). There was no significant correlation regionally between the SampEn maps and age. These results support the Goldberger/Lipsitz model for robustness and have shown that fApEn is potentially a sensitive new method for the complexity analysis of fMRI data.

Modeling Metabolism and Disease in Bioarcheology

We examine two important measures that can be made in bioarcheology on the remains of human and vertebrate animals. These remains consist of bone, teeth, or hair; each shows growth increments and each can be assayed for isotope ratios and other chemicals in equal intervals along the direction of growth. In each case, the central data is a time series of measurements. The first important measures are spectral estimates in spectral analyses and linear system analyses; we emphasize calculation of periodicities and growth rates as well as the comparison of power in bands. A low frequency band relates to the autonomic nervous system (ANS) control of metabolism and thus provides information about the life history of the individual of archeological interest. Turning to nonlinear system analysis, we discuss the calculation of SM Pinus' approximate entropy (ApEn) for short or moderate length time series. Like the concept that regular heart R-R interval data may indicate lack of health, low values of ApEn may indicate disrupted metabolism in individuals of archeological interest and even that a tipping point in deteriorating metabolism may have been reached just before death. This adds to the list of causes of death that can be determined from minimal data.

Effects of sampling rate on automated fatigue recognition in surface EMG signals

This study investigated the effects different sampling rates may produce on the quality of muscle fatigue detection algorithms. sEMG signals were obtained from isometric contractions of the arm. Subsampled signals resulting in technically relevant sampling rates were computationally deduced from the original recordings. The spectral based fatigue recognition methods mean and median frequency as well as spectral moment ratio were included in this investigation, as well as the sample and the fuzzy approximate entropy. The resulting fatigue indices were evaluated with respect to noise and separability of different load levels. We concluded that the spectral moment ratio provides the best results in fatigue detection over a wide range of sampling rates.

Characterization of complexity in the electroencephalograph activity of Alzheimer's disease based on fuzzy entropy

In this paper, experimental neurophysiologic recording and statistical analysis are combined to investigate the nonlinear characteristic and the cognitive function of the brain. Fuzzy approximate entropy and fuzzy sample entropy are applied to characterize the model-based simulated series and electroencephalograph (EEG) series of Alzheimer's disease (AD). The effectiveness and advantages of these two kinds of fuzzy entropy are first verified through the simulated EEG series generated by the alpha rhythm model, including stronger relative consistency and robustness. Furthermore, in order to detect the abnormality of irregularity and chaotic behavior in the AD brain, the complexity features based on these two fuzzy entropies are extracted in the delta, theta, alpha, and beta bands. It is demonstrated that, due to the introduction of fuzzy set theory, the fuzzy entropies could better distinguish EEG signals of AD from that of the normal than the approximate entropy and sample entropy. Moreover, the entropy values of AD are significantly decreased in the alpha band, particularly in the temporal brain region, such as electrode T3 and T4. In addition, fuzzy sample entropy could achieve higher group differences in different brain regions and higher average classification accuracy of 88.1% by support vector machine classifier. The obtained results prove that fuzzy sample entropy may be a powerful tool to characterize the complexity abnormalities of AD, which could be helpful in further understanding of the disease.