High Frequency Components Research Articles

Impact of Gonadectomy on Sex‐Selective Dysregulation of Cardiovascular Function Resulting From In UteroExposure to Dexamethasone

Prenatal insults leading to increased fetal glucocorticoid exposure can sex‐selectively impact cardiovascular function in adulthood. In support of this, we have shown that when pregnant rat dams are treated with the glucocorticoid, dexamethasone (DEX), for the last 4 days of gestation, female‐specific changes resulting in enhanced pressor and heart rate (HR) responses to stress occur in adult offspring. Moreover, we demonstrated that females exposed to DEX in uteroshow altered autonomic function compared to vehicle‐exposed females. This is characterized by a reduced high frequency (HF) power component of heart rate variability (HRV) in DEX‐exposed rats, suggesting a lower reliance on parasympathetic drive. The present study evaluated the degree to which the sex‐specific changes in autonomically‐driven cardiovascular responses are due to activational effects of gonadal steroid hormones. Pregnant dams were administered DEX (0.4mg/kg per day, s.c.) or vehicle on gestation days 18‐21. This resulted in a significant reduction in birthweight in DEX‐exposed males and females. At 8 weeks, rats underwent a gonadectomy (GDX) or sham surgery, or remained intact, and at 10 weeks rats were instrumented with radiotelemetric transmitters for direct recording of arterial pressure, HR, and HRV in conscious, freely moving male and female rats. At 11‐12 weeks rats were placed in a restraint tube for 20 minutes, followed by a 3‐hour recovery period, to assess whether GDX alters the sex‐specific stress responses in DEX‐exposed offspring. Restraint‐stress testing was performed on diestrus in intact and sham females, and absence of cycling in GDX females was confirmed via cytological analysis. We demonstrate that intact females, but not males, that were exposed to DEX in utero exhibit an exaggerated pressor response to restraint, as compared to vehicle‐exposed females. We found that GDX did not alter stress‐responsive MAP in males regardless of prenatal treatment, suggesting testosterone does not play a role in acute cardiovascular stress responses in adult rats. In vehicle‐exposed females, when compared to intact, both sham and GDX surgery resulted in an exaggerated pressor response to restraint. However, in females that were prenatally exposed to DEX, there was no difference in the pressor response to restraint between intact, sham, and GDX rats. This suggests that the exaggerated pressor response observed in DEX females compared to males is not due to activational effects of estradiol. GDX was shown to have opposing effects on HRV in males and females, with no influence of prior DEX. In females, GDX resulted in a tendency toward reduced HF (p=0.065) and low frequency (LF, p=0.074) power component of HRV, whereas males showed a significant increase in HF (p=0.044) and a tendency toward increased LF (0.051). Taken together, activational effects of gonadal steroids appear to impact HRV, but not acute cardiovascular responses to stress. It may be that gonadal steroids act at an organizational level to mediate the sex difference observed in rats exposed to DEX in utero. Future studies to identify the mechanisms by which prenatal dexamethasone produce long‐term changes in cardiovascular function will be important for better understanding the sex‐specific consequences of prenatal programming over the lifespan.

Heart rate variability during different phases of menstrual cycle

The purpose of the present study was to determine whether there is a menstrual cycle effect on heart rate variability. Ten healthy regularly cycling females (age 19‐23 years) were studied during the follicular phase (FP) and luteal phase (LP) of menstrual cycle over two month. We found significant changes in heart rate (65,35±0,68 beats/min in FP vs 73,18±0,92 beats/min in LP, p<0,0005), AMo and stress index during the menstrual cycle with a minimum in the follicular phase (36,81±1,14 % and 76,86±5,74 p.d.u. respectively) and maximum in the luteal phase (42,05±1,27 % and 107,70±7,77 p.d.u. respectively, p<0,003). The HF and LF components decreased more during the luteal phase (655,56±46,64 ms2 and 981,85±66,64 ms2 respectively) than during the follicular phase (997,42±83,36 ms2 and 1524,66±145,17 ms2 respectively, p<0,05). In the follicular phase SDNN, pNN50, MxDMn were significantly higher (0,057±0,002 s, 41,49±1,79 % and 0,333±0,012 s respectively) than in the luteal phase (0,051±0,002 s, 29,73±1,88 % and 0,303±0,012 s respectively, p<0,05). Furthermore, the VIK was higher in the luteal phase compared to the follicular phase (p=0,003). These findings indicate that sympathetic nervous activity in the luteal phase is greater than in the follicular phase, whereas parasympathetic nervous activity is predominant in the follicular phase. A difference of the balance of ovarian hormones may be responsible for these changes of autonomic functions during the menstrual cycle.

Multiplicative Error Models: 20 years on

The issue of combining low– and high–frequency components of volatility is addressed within the class of Multiplicative Error Models both in the univariate and multivariate cases. Inference based on the Generalized Method of Moments is suggested, which has the advantage of not requiring a parametric choice for the error distribution. The application relates to several volatility market indices (US, Europe and East Asia, with interdependencies in the short–run components of absolute returns, realized kernel volatility and option–based implied volatility indices): a set of diagnostic tools is used to evaluate the evidence of a relevant low–frequency component across markets, also from a forecasting comparison perspective. The results show that the slow–moving component in the dynamics achieves a better fit to the data and allows for an interpretation of what moves the local average level of volatility.

Application of Intensity-Based Coherent Optical Time Domain Reflectometry to Bridge Monitoring.

Although distributed fiber sensing techniques have been widely used in structural health monitoring, the measurement results of bridge monitoring, particularly under destructive testing, have rarely been reported. To the best of our knowledge, this paper is the first report of distributed vibration measurement results, which we obtained during a three-day destructive test on an abolished bridge. A coherent optical time domain reflectometry (COTDR) was used to acquire the vibration information while the bridge was being sawed. The obtained signal was analyzed in time and frequency domain. Some characteristics of the sawing-induced vibration were retrieved by the short-time Fourier transform; the vibration exhibited several high frequency components within the measured range up to 20 kHz and all the components appeared in the same time slot. Some unexpected signals were also detected. Thorough analysis showed that they are quite different from the sawing-induced vibration and are believed to originate from internal damage to the bridge (probably the occurrence of cracks).

A new high-frequency-based method for the very fast differential protection of power transformers

A novel algorithm based on Teager energy operator (TEO) and hidden Markov model (HMM) is presented for the transformer differential protection. After detecting any increase in the amplitude of the transformer differential currents, it is needed to know if the over-current is because of an internal fault or an inrush phenomenon. In this paper, a combination of TEO and HMM is used to discriminate between the magnetizing inrush currents and the internal fault currents. TEO is used to extract high frequency components of the signal and present them as a curve, named as Teager output curve (TOC). Then TOC is used as the input for HMM. First, the HMM training process is implemented by an adequate number of the train signals and then, the unknown signals are identified by the testing process. Fault type identification is also performed by the use of TOC. TOC has a separated region for each of the three phases. TEO of the zero-sequence signal has a peak for the grounded and no peak for the un-grounded faults. Train and test signals are generated by the simulated system in PSCAD/EMTDC. Results by means of the simulated and experimental data show the robustness of the proposed algorithm.

An adapted deflated conjugate gradient solver for robust extended/generalised finite element solutions of large scale, 3D crack propagation problems

An adapted deflation preconditioner is employed to accelerate the solution of linear systems resulting from the discretisation of fracture mechanics problems with well-conditioned extended/generalised finite elements. The deflation space typically used for linear elasticity problems is enriched with additional vectors, accounting for the enrichment functions used, thus effectively removing low frequency components of the error. To further improve performance, deflation is combined, in a multiplicative way, with a block-Jacobi preconditioner, which removes high frequency components of the error as well as near-linear dependencies introduced by enrichment. The resulting scheme is tested on a series of non-planar crack propagation problems and compared to alternative linear solvers in terms of performance.

Differences between human and machine perception in medical diagnosis

Deep neural networks (DNNs) show promise in image-based medical diagnosis, but cannot be fully trusted since they can fail for reasons unrelated to underlying pathology. Humans are less likely to make such superficial mistakes, since they use features that are grounded on medical science. It is therefore important to know whether DNNs use different features than humans. Towards this end, we propose a framework for comparing human and machine perception in medical diagnosis. We frame the comparison in terms of perturbation robustness, and mitigate Simpson’s paradox by performing a subgroup analysis. The framework is demonstrated with a case study in breast cancer screening, where we separately analyze microcalcifications and soft tissue lesions. While it is inconclusive whether humans and DNNs use different features to detect microcalcifications, we find that for soft tissue lesions, DNNs rely on high frequency components ignored by radiologists. Moreover, these features are located outside of the region of the images found most suspicious by radiologists. This difference between humans and machines was only visible through subgroup analysis, which highlights the importance of incorporating medical domain knowledge into the comparison.

Future evolution of global land surface air temperature trend based on Coupled Model Intercomparison Project Phase 6 models

AbstractIn recent decades, global warming has been an indisputable fact. The increase in extreme events accompanied by the rise in temperature has huge influences on many aspects of the society and economy globally. As a result, forecasting how global temperatures will change and evolve in the future is crucial, but it is restricted by the accuracy and uncertainty of model simulations. Based on ensemble empirical mode decomposition (EEMD) method, this study first evaluates the performance of the CMIP6 models in simulating different timescale components including high‐frequency components (HFC), low‐frequency components (LFC), and secular trend (ST) of surface air temperature (SAT). The results show that the performance of the CMIP6 models in simulating the ST is better. Following the observation constraint method's correction of CMIP6 model simulations, we investigate the evolution characteristics of future temperature secular trends under different Shared Socioeconomic Pathway (SSP) scenarios. The results show that under SSP245 scenario, the rise of SAT first appeared in northwest and east North America and eastern Europe. The spatial distribution of evolution characteristic under the SSP585 scenario is similar to that of the SSP245 scenario but with a larger magnitude. However, more pronounced differences appear in warming rate, which shows a downward trend over time under the SSP245 scenario and an upward trend with time under the SSP585 scenario.

Commodity markets dynamics: What do cross-commodities over different nearest-to-maturities tell us?

In this paper we investigate cross-commodity futures markets connectedness over different nearest-to-maturities. We thus implement time and time-frequency estimations for two constructed baskets of commodities, classified based on common delivery months. Using daily data spanning the period 1995–2020, we provide a set of stylized facts on the extent to which commodity markets are integrated or segmented. More specifically, our results show that the total connectedness is broadly insensitive to maturity. However, after 2008 financial crisis, the connectedness among commodity futures prices increases when the maturity increases. Furthermore, the overall connectedness amplifies during crises periods compared to tranquil periods. Moreover, certain pairwise markets are comparatively highly linked such as crude oil and heating oil, wheat and corn, corn and soybean, and soybean and soybean oil. The results also demonstrate that crude oil and heating oil are net transmitters all the time and across maturities, while natural gas, gold, and wheat are net receivers all the time and across maturities. More interestingly, the frequency decomposition reveals that most of periods of high total connectedness are driven mostly by high frequency components, which may indicate that commodity markets process information rapidly, except for the COVID-19 crisis period where total connectedness has been driven by lower frequency components.

Hybrid self-calibration method for reference surface elimination in subaperture stitching interferometry

Subaperture stitching interferometry (SSI) is an essential method for the map testing of large-aperture optical components. The surface map of a reference surface is a typical error source. In this study, we propose a hybrid self-calibration method to eliminate the reference surface error in SSI by combining the modified shift-rotation method and the maximum likelihood method. The traditional shift-rotation method is a general full-aperture absolute interferometric measurement that can retain localized irregularities. The shift-rotation operations are leveraged to generate a couple of subapertures covering the surface under test, whereby a ring of subapertures and a central subaperture are acquired by rotations and a lateral shift, respectively. The modified shift-rotation method is proposed to obtain the rotationally asymmetric components of the test surface in the ring of subapertures. The same components within the central subaperture are retrieved using the maximum likelihood method. Then, the rotationally symmetric components of the test surface are acquired using the least squares method, utilizing the measured data before and after the shift. Reference surface maps are sufficiently eliminated from the measured data. High-frequency components of the test surface are also retained, which engender high-accuracy SSI. Simulations are conducted to verify the proposed method. The positioning errors of the proposed method are analysed and discussed. Subaperture testing experiments of a 100-mm aperture flat are performed and compared with full-aperture absolute measurement results. The stitched errors with 0.018 λ PV and 0.003 λ RMS are obtained.

Sympathovagal equilibrium analysis in patients with COVID-19

Introduction: With the increase of COVID-19 cases, an unusual manifestation for this type of virus began to appear anosmia and dysgeusia, which could indicate a neurologic alteration. In this context, it seems likely that subclinical manifestations of baroreflex involvement occur. The vegetative nervous system carries out the regulation of the baroreflex through the balance between sympathetic and parasympathetic activity. The objective of this study is to verify whether patients with COVID-19 present alteration of this equilibrium. Material and methods: Patients included had a confirmed diagnosis of COVID-19 admitted to the Internal Medicine Department of JB Iturraspe Hospital. A Holter recording was performed at rest for 5 minutes, determining the variables in the frequency domain using Fourier transform analysis. We excluded patients with diabetes, medicated with drugs that modify heart rate or with a history of irradiation to the neck. Results: 68 patients were studied. The mean age was 49±13 years. The median systolic blood pressure was 120 mmHg and the diastolic blood pressure 80 mmHg. The heart rate was 76±13 beats per minute and the median respiratory rate was 24 (16 to 40). Anosmia was observed in 22% and dysgeusia in 19% The variables in the frequency domain were: Low-frequency power (LF) 135.8ms2 (13.7-2861.7); High-frequency power (HF), 89.04ms2 (4.1-5234.4), LFnu 57.5±22.3, HFnu 43.1±22.6. LF:HF 2.1±2. 41.2% of the patients had a high LF:HF. Conclusions: LF and HF components can be obtained through frequency analysis. The relationship between these two elements would thus represent the sympathovagal balance and is expressed as the LF/HF ratio. We observed that 41.2% of the studied patients showed elevated LF/HF ratio. The 41.2% of the patients presented an increased LF:HF ratio, which could be interpreted as an alteration in autonomic function.

Study on the distribution characteristics and uncertainty of multiple energy load patterns for building group to enhance demand side management

The energy consumption data of buildings contain plenty of knowledge that can be used to enhance demand side management (DSM). However, existing research rarely conducts comprehensive analysis on the multiple energy characteristics for the same building group simultaneously. Additionally, DSM requires the load side to meet the flexibility demands at multiple time scales. To address these problems, the paper presents a strategy with two-stage clustering to recognize multiple energy load patterns of higher education buildings at different time scales. In this work, the low frequency component (LFC) and high frequency component (HFC) are obtained from daily load curve by wavelet decomposition, which are clustered by two-stage clustering to construct typical patterns of load at different time scales. Based on these patterns, a series of exploratory studies on their characteristics are launched. The influence characteristic of factors on multiple energy load patterns are revealed. The results show that different functional buildings have obvious differences in the distribution of their electrical LFC patterns on working days and nonworking days, and the shapes of the dominant patterns of power load and cooling load are more consistent with the change of the ambient temperature throughout the day. In addition, the uncertainty of the load pattern is quantified by entropy, which is used to reflect the order degree of the load pattern change over time for each building. The results indicate that probability of a pattern remaining unchanged is higher than transitioning to other patterns, and the uncertainty of HFC pattern for power load is generally higher than that of LFC. The results obtained from this study could be potentially used to enhance DSM.

Comparison of autonomic functions in moderate and heavy smokers using heart rate variability

Previous studies using heart rate variability (HRV) have confirmed decreased HRV in cigarette smokers compared with non smokers. However only few studies have been done considering the effect of smoking on HRV in moderate and severe smokers assessed by pack years. Objective: The main objective of this study was to compare the autonomic functions in male moderate and heavy cigarette smokers.The present was conducted in our department of Physiology. Permission to conduct the study was obtained from Institutional research ethical committee. This study was conducted among male cigarette smokers with age between 20 to 50 years who were apparently healthy. The study group subjects were divided into moderate and heavy smokers considering both the amount and duration of smoking calculated by pack years. HRV data was recorded, statistical analysis was done by independent sample t test to compare between the groups. A p value of <0.05 was considered as statistically significant whereas a p value <0.001 was considered as highly significant.The HRV parameters namely Low frequency (LF) component, LF power expressed in normalized unit (LFnu) and LF-HF ratio were significantly higher in heavy smokers compared to moderate smokers. Total power, high frequency (HF) component and HF power expressed in normalized unit (HFnu) were found significantly less in heavy smokers compared to moderate smokers. Autonomic imbalance characterized by increased sympathetic function with associated decrease in the cardiac vagal modulation was seen in heavy smokers compared to moderate smokers.

A DSP-Based EIT System With Adaptive Boundary Voltage Acquisition

Electrical Impedance Tomography (EIT), a valuable technique modality for estimating electrical properties of an object by electrode measurements. During the measurement process, high-frequency excitation signal and the large dynamic range of boundary voltage both cause negative effects on system detection accuracy. Focusing on the induced signal detection, we proposed an adaptive boundary voltage acquisition method, which thoroughly modeled the EIT system by LTSPICE and MATLAB, dynamically adjusted the boundary voltage amplitude gain according to the induced signal voltage range, and changed sampling period for high frequency component of excitation signal. A DSP-based EIT hardware experimental platform under development was constructed, including a current source integrated direct digital synthesis and data acquisition circuit. The data acquisition circuit composed of programmable differential amplifier, low-pass filter and offset adjustment circuit is used for signal acquisition. Comparative experiments are systematically implemented for evaluating the proposed method. Quantitatively, the SNR difference of each measurement channel is reduced from 26.42dB to 6.33dB. The maximum SNR of the system can reach 60.67dB. The maximum image error (IE) is reduced from 28.17% to 15.60%. Accordingly, the proposed adaptive boundary voltage acquisition method is proved to be great beneficial to improve EIT system accuracy.

Spectrum evolution and chirping of laser-induced spin wave packets in thin iron films

We present an experimental study of ultrafast optical excitation of magnetostatic surface spin wave (MSSW) packets and their spectral properties in thin films of pure iron. As the packets leave the excitation area and propagate in space, their spectra evolve non-trivially. Particularly, low or high frequency components are suppressed at the border of the excitation area depending on the orientation of the external magnetic field with respect to the magnetocrystalline anisotropy axes of the film. The effect is ascribed to the ultrafast local heating of the film. Furthermore, the time resolution of the implemented all-optical technique allows us to extract the chirp of the MSSW packet in the time domain via wavelet analysis. The chirp is a result of the group velocity dispersion of the MSSW and, thus, is controlled by the film's magnetic parameters, magnetization and anisotropy, and external field orientation. The demonstrated tunable modulation of MSSW wave packets with femtosecond laser pulses may find application in future magnonic-photonic hybrid devices for wave-based data processing.

Remote Sensing Image Denoising Based on Deep and Shallow Feature Fusion and Attention Mechanism

Optical remote sensing images are widely used in the fields of feature recognition, scene semantic segmentation, and others. However, the quality of remote sensing images is degraded due to the influence of various noises, which seriously affects the practical use of remote sensing images. As remote sensing images have more complex texture features than ordinary images, this will lead to the previous denoising algorithm failing to achieve the desired result. Therefore, we propose a novel remote sensing image denoising network (RSIDNet) based on a deep learning approach, which mainly consists of a multi-scale feature extraction module (MFE), multiple local skip-connected enhanced attention blocks (ECA), a global feature fusion block (GFF), and a noisy image reconstruction block (NR). The combination of these modules greatly improves the model’s use of the extracted features and increases the model’s denoising capability. Extensive experiments on synthetic Gaussian noise datasets and real noise datasets have shown that RSIDNet achieves satisfactory results. RSIDNet can improve the loss of detail information in denoised images in traditional denoising methods, retaining more of the higher-frequency components, which can have performance improvements for subsequent image processing.

A high frequency builder software for arbitrary radio frequency signals.

While the frequencies accessible by signal generators steadily rise, the synthesization of complex and arbitrary waveforms with high frequency components remains challenging, especially when restricted by an external reference clock. In this article, we present a comprehensive software package combined with state-of-the-art hardware as a solution for the generation of highly sampled, arbitrary radio frequency waveforms. The software can be used to conduct both synchronous and heterodyne pump-probe experiments due to a variety of different synchronization modules. While both kinds of modules allow for standard waveforms, such as sines, pulses, and bursts, as well as any arbitrary signal, the heterodyne modules additionally are not restricted by the reference clock frequency. Both the output and the synchronization module can be adapted to support additional measurement devices. Due to the modular software structure, individual classes can be exchanged while maintaining all functionalities. The software provides a user friendly graphical interface that allows us to compose, save, and load complex arbitrary waveforms within only a few steps. The frequency selectivity provided by the software-hardware combination allows us to directly target specific excitation states of physical systems. Conducting a heterodyne scanning transmission x-ray microscopy experiment, we are able to demonstrate the capabilities of the software when paired with a high sample rate arbitrary waveform generator. The heterodyne synchronization modules allow for unlimited flexibility leveraging arbitrary waveform generation to their full power. By solving the challenges of synthesizing highly complex electromagnetic waves, the software enables a large variety of experiments to be performed more conveniently.

Control strategy of wind power smooth grid connection based on adaptive VMD and hybrid energy storage

In nature, the variation of wind speed is characterized by randomness, fluctuation, and intermittence. In order to suppress the power fluctuation caused by wind speed changes in the process of wind turbine grid connection, a wind power smooth grid-connected control strategy based on the adaptive variational modal decomposition algorithm and the hybrid energy storage system is proposed. For the problem that the selection of variational modal decomposition parameters is subjective and experiential, which leads to the poor signal decomposition reduction degree, the sparrow search algorithm is proposed to optimize variational modal decomposition to realize the adaptive selection of key variational modal decomposition parameters k and α. First, the reference power of the hybrid energy storage system conforming to the grid-connected power fluctuation standard is obtained by the adaptive moving average algorithm. Then, adaptive variational modal decomposition of the reference power was performed to obtain a set of inherent modal functional components, and the low frequency and high frequency components of the modal components were allocated for lithium batteries and the supercapacitor, respectively. Finally, Matlab/Simulink was used to simulate and compare with the control strategy of low-pass filtering. The results show that the proposed algorithm realizes the selection of adaptive decomposition parameters of variational modal decomposition, solves the lag and frequency aliasing problems existing in power distribution of the low-pass filtering algorithm, and realizes the high reduction solution of reference power. The fluctuation of wind power connected to the grid is effectively suppressed.

Hybrid Pseudorandom Signal Injection for Position Sensorless SynRM Drives With Acoustic Noise Reduction

The extra acoustic noise caused by the high-frequency (HF) signal injection imposes restrictions on its extensive applications for sensorless synchronous reluctance motor (SynRM) drives. To attenuate the undesired acoustic noise further, a hybrid pseudorandom (PR) signal injection scheme is proposed in this article. The hybrid PR signal injection is implemented by injecting square wave signals chosen in a random manner from a set of signals having either different frequencies or phases from each other. The optimal assembly of hybrid PR signals is designed via current power spectral density (PSD) analysis, aiming at reducing acoustic noise as much as possible. In this way, the proposed scheme achieves success in a massive acoustic noise reduction by means of completely eliminating the discrete harmonics of the current PSD and distributing the current PSD to a wider range. Besides, the low-pass filters used to separate the fundamental frequency component from injected HF components of currents could be eliminated because no discrete harmonics at injected frequencies exist with the proposed hybrid PR signal injection. The validity and superiority of the proposed sensorless control scheme are verified on a 3-kW position sensorless SynRM drive platform.

Proposed Fatigue Index for the Objective Detection of Muscle Fatigue Using Surface Electromyography and a Double-Step Binary Classifier.

The objective detection of muscle fatigue reports the moment at which a muscle fails to sustain the required force. Such a detection prevents any further injury to the muscle following fatigue. However, the objective detection of muscle fatigue still requires further investigation. This paper presents an algorithm that employs a new fatigue index for the objective detection of muscle fatigue using a double-step binary classifier. The proposed algorithm involves analyzing the acquired sEMG signals in both the time and frequency domains in a double-step investigation. The first step involves calculating the value of the integrated EMG (IEMG) to determine the continuous contraction of the muscle being investigated. It was found that the IEMG value continued to increase with prolonged muscle contraction and progressive fatigue. The second step involves differentiating between the high-frequency components (HFC) and low-frequency components (LFC) of the EMG, and calculating the fatigue index. Basically, the segmented EMG signal was filtered by two band-pass filters separately to produce two sub-signals, namely, a high-frequency sub-signal (HFSS) and a low-frequency sub-signal (LFSS). Then, the instantaneous mean amplitude (IMA) was calculated for the two sub-signals. The proposed algorithm indicates that the IMA of the HFSS tends to decrease during muscle fatigue, while the IMA of the LFSS tends to increase. The fatigue index represents the difference between the IMA values of the LFSS and HFSS, respectively. Muscle fatigue was found to be present and was objectively detected when the value of the proposed fatigue index was equal to or greater than zero. The proposed algorithm was tested on 75 EMG signals that were extracted from 75 middle deltoid muscles. The results show that the proposed algorithm had an accuracy of 94.66% in distinguishing between conditions of muscle fatigue and non-fatigue.