This document presents research on evaluating frequency domain features for myopathic electromyography (EMG) signals. The researchers analyzed healthy and myopathic EMG signals in the frequency domain. They calculated the power spectral density of the signals using Welch's power spectral density estimate method with Hamming and Kaiser windows. The power spectral densities of the healthy and myopathic signals were compared. The analysis provided insights that could help design feature extraction methods to determine the origin of muscle weakness as neurogenic or myopathic.
This document presents a comparative analysis of healthy and neuropathic EMG signals using power spectral density (PSD). The study analyzes EMG signals from healthy muscles and those affected by neuropathy. It uses Welch's PSD estimation method with Hamming and Kaiser windows to compare the distribution of power over frequency components between the two signal types. The analysis found that neuropathic signals had a broader distribution over frequencies compared to healthy signals. This type of spectral analysis could help differentiate between normal and neuropathic muscle conditions for diagnosing neuropathies.
Correlation Analysis of Electromyogram SignalsIJMTST Journal
An inability to adapt myoelectric interfaces to a user’s unique style of hand motion. The system also adapts
the motion style of an opposite limb. These are the important factors inhibiting the practical application of
myoelectric interfaces. This is mainly attributed to the individual differences in the exhibited electromyogram
(EMG) signals generated by the muscles of different limbs. In this project myoelectric interface easily adapts
the signal from the users and maintains good movement recognition performance. At the initial stage the
myoelectric signal is extracted from the user by using the data acquisition system. A new set of features
describing the movements of user’s is extracted and the user’s features are classifed using SVM
classification. The given signal is then compared with the database signal with the accuracy of 90.910 %
across all the EMG signals.
A SIMPLE METHOD TO AMPLIFY MICRO DISPLACEMENTijics
This document describes a simple method for amplifying micro displacements produced by various effects, including magnetostriction, piezoelectric, and photostrictive effects. The method involves rigidly joining two material rods with different strain coefficients when exposed to an external field. When a field is applied, one rod expands while the other contracts, adding their displacements. This direct addition allows much larger displacements without lever mechanisms that introduce friction. Examples demonstrate amplification of displacements from micrometers to millimeters using magnetostrictive and piezoelectric material pairs. The approach requires no moving parts, enabling response to high-frequency fields without phase delay.
This honors thesis presents two proofs of concept using Gaussian process regression and tactile feedback to teach dynamic systems new motions. The first is a computer simulation of a pendulum learning to control itself. The second is a physical robotic pendulum that learns to control itself and can be taught new motions through force feedback. Both aim to test if GPR can be used to control dynamic systems and if tactile feedback can teach new motions. This research is a step toward developing methods for caregivers to teach arm neuroprosthesis users new motions through touch.
Embedded system for upper-limb exoskeleton based on electromyography controlTELKOMNIKA JOURNAL
A major problem in an exoskeleton based on electromyography (EMG) control with pattern recognition-based is the need for more time to train and to calibrate the system in order able to adapt for different subjects and variable. Unfortunately, the implementation of the joint prediction on an embedded system for the exoskeleton based on the EMG control with non-pattern recognition-based is very rare. Therefore, this study presents an implementation of elbow-joint angle prediction on an embedded system to control an upper limb exoskeleton based on the EMG signal. The architecture of the system consisted of a bio-amplifier, an embedded ARMSTM32F429 microcontroller, and an exoskeleton unit driven by a servo motor. The elbow joint angle was predicted based on the EMG signal that is generated from biceps. The predicted angle was obtained by extracting the EMG signal using a zero-crossing feature and filtering the EMG feature using a Butterworth low pass filter. This study found that the range of root mean square error and correlation coefficients are 8°-16° and 0.94-0.99, respectively which suggest that the predicted angle is close to the desired angle and there is a high relationship between the predicted angle and the desired angle.
The document reports on experiments measuring the interaction of a gravity impulse beam with light and determining the propagation speed of the gravity impulse. The key findings are:
1) Laser light intensity was found to decrease by 2.8-7.5% for 34-48 ns when interacting with the gravity beam, with attenuation increasing with discharge voltage.
2) The propagation time of the gravity impulse over 1211 m, as measured by piezoelectric sensors connected to atomic clocks, was 63±1 ns, corresponding to a speed of (64±1)c.
3) Theoretical analysis suggests the beam consists of virtual particles with finite lifetimes. Different targets may absorb components propagating at different velocities, complic
1) The document presents an experimental validation of a super-sensitive chemical imaging technique called multiphoton frequency-domain fluorescence lifetime imaging microscopy (MPM-FD-FLIM).
2) The experiments demonstrate a 2x improvement in imaging speed compared to the theoretical limit of conventional MPM-FD-FLIM. Additionally, unprecedented sensitivity is achieved over a wide range of fluorescence lifetimes.
3) These results are obtained through simple modifications to data analysis in a conventional MPM-FD-FLIM microscope based on an analytical model describing the signal-to-noise ratio of such systems. The experimental results validate this theoretical model.
This document summarizes a research article that develops a theoretical model to describe how decoherence effects rubidium vapor in an electromagnetically induced transparency (EIT) experiment. The model accounts for decoherence from both dephasing and population relaxation. It quantifies the impact of decoherence on various experimental measurements, including Faraday rotation, susceptibility, transmission, and coherence relationships. The model is in good agreement with previous experimental results. It also discusses how the model could be applied to other EIT-based experiments and how Faraday rotation could be used to detect single atoms.
This document presents a comparative analysis of healthy and neuropathic EMG signals using power spectral density (PSD). The study analyzes EMG signals from healthy muscles and those affected by neuropathy. It uses Welch's PSD estimation method with Hamming and Kaiser windows to compare the distribution of power over frequency components between the two signal types. The analysis found that neuropathic signals had a broader distribution over frequencies compared to healthy signals. This type of spectral analysis could help differentiate between normal and neuropathic muscle conditions for diagnosing neuropathies.
Correlation Analysis of Electromyogram SignalsIJMTST Journal
An inability to adapt myoelectric interfaces to a user’s unique style of hand motion. The system also adapts
the motion style of an opposite limb. These are the important factors inhibiting the practical application of
myoelectric interfaces. This is mainly attributed to the individual differences in the exhibited electromyogram
(EMG) signals generated by the muscles of different limbs. In this project myoelectric interface easily adapts
the signal from the users and maintains good movement recognition performance. At the initial stage the
myoelectric signal is extracted from the user by using the data acquisition system. A new set of features
describing the movements of user’s is extracted and the user’s features are classifed using SVM
classification. The given signal is then compared with the database signal with the accuracy of 90.910 %
across all the EMG signals.
A SIMPLE METHOD TO AMPLIFY MICRO DISPLACEMENTijics
This document describes a simple method for amplifying micro displacements produced by various effects, including magnetostriction, piezoelectric, and photostrictive effects. The method involves rigidly joining two material rods with different strain coefficients when exposed to an external field. When a field is applied, one rod expands while the other contracts, adding their displacements. This direct addition allows much larger displacements without lever mechanisms that introduce friction. Examples demonstrate amplification of displacements from micrometers to millimeters using magnetostrictive and piezoelectric material pairs. The approach requires no moving parts, enabling response to high-frequency fields without phase delay.
This honors thesis presents two proofs of concept using Gaussian process regression and tactile feedback to teach dynamic systems new motions. The first is a computer simulation of a pendulum learning to control itself. The second is a physical robotic pendulum that learns to control itself and can be taught new motions through force feedback. Both aim to test if GPR can be used to control dynamic systems and if tactile feedback can teach new motions. This research is a step toward developing methods for caregivers to teach arm neuroprosthesis users new motions through touch.
Embedded system for upper-limb exoskeleton based on electromyography controlTELKOMNIKA JOURNAL
A major problem in an exoskeleton based on electromyography (EMG) control with pattern recognition-based is the need for more time to train and to calibrate the system in order able to adapt for different subjects and variable. Unfortunately, the implementation of the joint prediction on an embedded system for the exoskeleton based on the EMG control with non-pattern recognition-based is very rare. Therefore, this study presents an implementation of elbow-joint angle prediction on an embedded system to control an upper limb exoskeleton based on the EMG signal. The architecture of the system consisted of a bio-amplifier, an embedded ARMSTM32F429 microcontroller, and an exoskeleton unit driven by a servo motor. The elbow joint angle was predicted based on the EMG signal that is generated from biceps. The predicted angle was obtained by extracting the EMG signal using a zero-crossing feature and filtering the EMG feature using a Butterworth low pass filter. This study found that the range of root mean square error and correlation coefficients are 8°-16° and 0.94-0.99, respectively which suggest that the predicted angle is close to the desired angle and there is a high relationship between the predicted angle and the desired angle.
The document reports on experiments measuring the interaction of a gravity impulse beam with light and determining the propagation speed of the gravity impulse. The key findings are:
1) Laser light intensity was found to decrease by 2.8-7.5% for 34-48 ns when interacting with the gravity beam, with attenuation increasing with discharge voltage.
2) The propagation time of the gravity impulse over 1211 m, as measured by piezoelectric sensors connected to atomic clocks, was 63±1 ns, corresponding to a speed of (64±1)c.
3) Theoretical analysis suggests the beam consists of virtual particles with finite lifetimes. Different targets may absorb components propagating at different velocities, complic
1) The document presents an experimental validation of a super-sensitive chemical imaging technique called multiphoton frequency-domain fluorescence lifetime imaging microscopy (MPM-FD-FLIM).
2) The experiments demonstrate a 2x improvement in imaging speed compared to the theoretical limit of conventional MPM-FD-FLIM. Additionally, unprecedented sensitivity is achieved over a wide range of fluorescence lifetimes.
3) These results are obtained through simple modifications to data analysis in a conventional MPM-FD-FLIM microscope based on an analytical model describing the signal-to-noise ratio of such systems. The experimental results validate this theoretical model.
This document summarizes a research article that develops a theoretical model to describe how decoherence effects rubidium vapor in an electromagnetically induced transparency (EIT) experiment. The model accounts for decoherence from both dephasing and population relaxation. It quantifies the impact of decoherence on various experimental measurements, including Faraday rotation, susceptibility, transmission, and coherence relationships. The model is in good agreement with previous experimental results. It also discusses how the model could be applied to other EIT-based experiments and how Faraday rotation could be used to detect single atoms.
Electron bunching in the optimal operating regime of a carcinotrodeVictor Solntsev
Electron bunching processes in a carcinotrode (backwardﱹwave oscillator with selfﱹmodulation of electron emission) operating in the highﱹefficiency regime determined previously are investigated. The posﱹ sibility of obtaining an efficiency of about 80% is explained from the physical viewpoint.
Dsp lab report- Analysis and classification of EMG signal using MATLAB.Nurhasanah Shafei
This document discusses a study analyzing and classifying electromyogram (EMG) signals. The researchers developed a MATLAB-based system that can differentiate EMG signals coming from different patients. The system analyzes time and frequency domain characteristics of the EMG signals, including median value, average value, root mean square, maximum power, and minimum power. It then uses these characteristics to identify which patient a given EMG signal belongs to through a graphical user interface. The system was able to accurately classify EMG signals from two patients based on their power spectrum signatures.
Nanostructured materials for magnetoelectronicsSpringer
This document discusses experimental approaches to studying magnetization and spin dynamics in magnetic systems with high spatial and temporal resolution.
It describes using time-resolved X-ray photoemission electron microscopy (TR-XPEEM) to image the temporal evolution of magnetization in magnetic thin films with picosecond time resolution. Results are presented showing the changing domain structure in a Permalloy thin film following excitation with a magnetic field pulse. Different rotation mechanisms are observed depending on the initial orientation of the magnetization with respect to the applied field.
A novel pump-probe magneto-optical Kerr effect technique using higher harmonic generation is also discussed for addressing spin dynamics in magnetic systems with femtosecond time resolution and element selectivity.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
The quality of data and the accuracy of energy generation forecast by artific...IJECEIAES
The paper presents the issues related to predicting the amount of energy generation, in a particular wind power plant comprising five generators located in south-eastern Poland. Thelocation of wind power plant, the distribution and type of applied generators, and topographical conditions were given and the correlation between selected weather parameters and the volume of energy generation was discussed. The primary objective of the paper was to select learning data and perform forecasts using artificial neural networks. For comparison, conservative forecasts were also presented. Forecasts results obtained shaw that Artificial Neural Networks are more universal than conservative method. However their forecast accuracy of forecasts strongly depends on the selection of explanatory data.
Development of Adaptive Neuro Fuzzy Inference System for Estimation of Evapot...ijsrd.com
The accuracy of an adaptive neurofuzzy computing technique in estimation of reference evapotranspiration (ETo) is investigated in this paper. The model is based on Adaptive Neurofuzzy Inference System (ANFIS) and uses commonly available weather information such as the daily climatic data, Maximum and Minimum Air Temperature, Relative Humidity, Wind Speed and Sunshine hours from station, Karjan (Latitude - 22°03'10.95"N, Longitude - 73°07'24.65"E), in Vadodara (Gujarat), are used as inputs to the neurofuzzy model to estimate ETo obtained using the FAO-56 Penman.Monteith equation. The daily meteorological data of two years from 2009 and 2010 at Karjan Takuka, Vadodara, are used to train the model, and the data in 2011 is used to predict the ETo in that year and to validate the model. The ETo in training period (Train- ETo) and the predicted results (Test-ETo) are compared with the ETo computed by Penman-Monteith method (PM-ETo) using "gDailyET" Software. The results indicate that the PM-ETo values are closely and linearly correlated with Train- ETo and Test- ETo with Root Mean Squared Error (RMSE) and showed the higher significances of the Train- ETo and Test- ETo. The results indict the feasibility of using the convenient model to resolve the problems of agriculture irrigation with intelligent algorithm, and more accurate weather forecast, appropriate membership function and suitable fuzzy rules.
Hybrid Algorithm for Dose Calculation in Cms Xio Treatment Planning SystemIOSR Journals
This study aimed at designing an improved hybrid algorithm by explicitly solving the linearized Boltzmann transport equation (LBTE) which is the governing equation that describes the macroscopic behaviour of radiation particles (neutrons, photons, electrons, etc). The algorithm accuracy will be evaluated using a newly designed in-house verification phantom and its results will be compared to those of the other XiO photon algorithms. The LBTE was solved numerically to compute photon transport in a medium. A programming code (algorithm) for the LBTE solution was developed and applied in the treatment planning system (TPS). The accuracy of the algorithm was evaluated by creating several plans for both the designed phantom and solid water phantom using the designed algorithm and other Xio photon algorithms. The plans were sent to a pre-calibrated Eleckta linear accelerator for measurement of absorbed dose.The results for all treatment plans using the hybrid algorithm compared to the 3 Xio photon algorithms were within 4 % limit. Calculation time for the hybrid algorithm was less in plans with larger number of beams compared to the other algorithms; however, it is higher for single beam plans. The hybrid algorithm provides comparable accuracy in treatment planning conditions to the other algorithms. This algorithm can therefore be employed in the calculation of dose in advance techniques such as IMRT and Rapid Arc by a radiotherapy centres with cmsxio treatment planning system as it is easy to implement.
Iaetsd recognition of emg based hand gesturesIaetsd Iaetsd
This document summarizes research on recognizing electromyography (EMG) signals from hand gestures to control prosthetics using artificial neural networks. EMG signals were collected from muscles during two hand gestures. Thirteen features were extracted from the signals and used to train and test several neural networks with different training algorithms. It was found that networks using the Levenberg-Marquardt algorithm achieved the best performance, with over 90% classification accuracy and the fastest training times, making it most suitable for accurate and rapid prosthetic control based on EMG pattern recognition.
Active vibration control of smart piezo cantilever beam using pid controllereSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Active vibration control of smart piezo cantilever beam using pid controllereSAT Journals
Abstract In this paper the modelling and Design of a Beam on which two Piezoelectric Ceramic Lead Zirconate Titanate ( PZT) patches are bonded on the top and bottom surface as Sensor/Actuator collocated pair is presented. The work considers the Active Vibration Control (AVC) using Proportional Integral Derivative (PID) Controller. The beam is assumed as Euler-Bernoulli beam. The two PZT patches are also treated as Euler-Bernoulli beam elements. The contribution of mass and stiffness of two PZT patches in the design of entire structure are also considered. The beam is modelled using three Finite Elements. The patches can be bonded near the fixed end, at middle or near the free end of the beam as collocated pair. The design uses first two dominant vibratory modes. The effect of PZT sensor/actuator pair is investigated at different locations of beam in vibration control. It can be concluded from the work that best result is obtained when the PZT patches are bonded near the fixed end. Keywords: Smart Beam, Active Vibration control, Piezoelectric, PID Controller, Finite Element
EMG Diagnosis using Neural Network Classifier with Time Domain and AR FeaturesIDES Editor
The shapes of motor unit action potentials
(MUAPs) in an electromyographic (EMG) signal
provide an important source of information for the
diagnosis of neuromuscular disorders. To extract this
information from the EMG signals, the first step is
identification of the MUAPs composed by the EMG
signal, second step is clustering of MUAPs with similar
shapes, third step is extraction of the features of MUAP
clusters and last step is classification of MUAPs. In this
work, the MUAPs are identified by using a data driven
segmentation algorithm, statistical pattern recognition
technique is used for clustering of MUAPs. Followed by
the extraction of time domain and autoregressive (AR)
features of the MUAP clusters. Finally, a neural
network (NN) classifier is used for classification of
MUAPs. A total of 12 EMG signals obtained from 3
normal (NOR), 5 myopathic (MYO) and 4 motor
neuron diseased (MND) subjects were analyzed. The
success rate for the segmentation technique is 95.90%
and for the statistical technique is 93.13%. The
classification accuracy of NN is 66.72% with time
domain parameters and 75.06 % with AR parameters.
This presentation discusses using single-molecule techniques like magnetic tweezers and tethered particle motion to study DNA mechanics and DNA-protein interactions. Single-molecule techniques allow direct observation and manipulation of individual molecules in real-time, unlike ensemble measurements. The presentation demonstrates these techniques on DNA structures like the double helix and G-quadruplex, and future work aims to use them to investigate polymerase activity and DNA-protein filament formation.
Abstract - Positioning is a fundamental component of human life to make meaningful interpretations of the environment. Without knowledge of position, human beings are like machines and have very limited capabilities to interact with the environment. Even machines in today’s world can be made smarter if positioning information is made available to them. Indoor positioning of pedestrians is the broad area considered in this thesis. A foot mounted pedestrian tracking device has been studied for this purpose. Systems which utilize foot mounted inertial navigation system has been in the literature for more than two decades. However very few real time implementations have been possible. The purpose of this thesis is to benchmark and improve the performance of one such implementation.
Mass Spectrometry: Protein Identification StrategiesMichel Dumontier
The document discusses protein identification strategies using mass spectrometry, including peptide mass fingerprinting (PMF) and tandem mass spectrometry. PMF involves comparing observed peptide masses from an unknown protein to theoretical masses in a database to identify matches, while tandem mass spectrometry fragments peptides and matches the fragmentation pattern to sequences in the database. The document provides details on how each technique works and their relative advantages and limitations.
This document discusses a new pulse model based iterative deconvolution (PMID) method for measuring the energy of scintillation events. The method models the scintillation detector as a linear system and treats energy measurement as a deconvolution problem. It uses an iterative MLEM algorithm to deconvolve digital scintillation pulses into spike signals, whose integrated voltages provide energy information. Experiments showed the PMID method provided better energy resolution than other methods for pileup events, and similar performance to digital gated integration for single events. The method is adaptive to different pulse shapes and can process pileups without detection.
IRJET-Electromyogram Signals for Multiuser Interface- A ReviewIRJET Journal
This document reviews various methods for feature extraction and classification of electromyogram (EMG) signals for multi-user myoelectric interfaces. It surveys previous work that used techniques like discrete wavelet transform (DWT) and support vector machines (SVM) for feature extraction and classification of EMG signals. The document concludes that DWT is well-suited for extracting both time and frequency domain features from non-stationary EMG signals. It also finds that SVM performed accurately for classification of features from multi-user EMG signals. The review aims to determine the best methods for a project using DWT for feature extraction and SVM for classification of EMG signals from multiple users.
A Detail Study of Wavelet Families for EMG Pattern Recognition IJECEIAES
Wavelet transform (WT) has recently drawn the attention of the researchers due to its potential in electromyography (EMG) recognition system. However, the optimal mother wavelet selection remains a challenge to the application of WT in EMG signal processing. This paper presents a detail study for different mother wavelet function in discrete wavelet transform (DWT) and continuous wavelet transform (CWT). Additionally, the performance of different mother wavelet in DWT and CWT at different decomposition level and scale are also investigated. The mean absolute value (MAV) and wavelength (WL) features are extracted from each CWT and reconstructed DWT wavelet coefficient. A popular machine learning method, support vector machine (SVM) is employed to classify the different types of hand movements. The results showed that the most suitable mother wavelet in CWT are Mexican hat and Symlet 6 at scale 16 and 32, respectively. On the other hand, Symlet 4 and Daubechies 4 at the second decomposition level are found to be the optimal wavelet in DWT. From the analysis, we deduced that Symlet 4 at the second decomposition level in DWT is the most suitable mother wavelet for accurate classification of EMG signals of different hand movements.
This document summarizes research on using artificial neural networks (ANNs) to automatically analyze and classify surface electromyography (SEMG) signals. The researchers:
1) Collected SEMG data from normal subjects and those with myopathies during muscle contractions. They extracted features using autoregressive (AR) modeling of signal segments.
2) Compared the classification performance of ANNs (backpropagation, self-organizing feature map, probabilistic neural network) to Fisher's linear discriminant analysis. The ANNs achieved over 90% correct classification while the linear method was poorer.
3) Concluded that properly processed SEMG combined with ANN classification can provide an automated diagnostic assist tool for physicians to help
This document presents a new method for estimating the spatial distribution of motor unit activity from high-density surface electromyography (HDsEMG) data. The method applies principal component compression and a rotatable Gaussian surface fit to map the center positions of motor unit territories. The method was tested on two subjects. With the first dataset, motor unit positions estimated from HDsEMG data were compared to the positions of intramuscular electrodes measured with ultrasound, showing good agreement. With the second dataset, distinct spatial distributions of motor units were identified for different muscle contractions. The study demonstrates that the proposed method can accurately estimate motor unit center positions from HDsEMG data and distinguish between individual muscles based on their spatial patterns of motor
This document describes a study that evaluates Laser Doppler Myography (LDM) as a non-contact method for measuring muscle contraction by comparing LDM signals to standard surface electromyography (sEMG) signals. LDM uses laser Doppler vibrometry to detect vibrations from contracting muscles without touching the skin. The study found that LDM and sEMG signals were similar in timing of muscle activation and that LDM signal amplitude increased with higher force production levels, suggesting LDM can reliably measure muscle contraction characteristics without contact. LDM may have advantages over sEMG such as being less prone to electrical artifacts and more directly related to muscle force.
Electromyography Analysis for Person IdentificationCSCJournals
Physiological descriptions of the electromyography signal and other literature say that when we make a motion, the motor neurons of respective muscle get activated and all the innervated motor units in that zone produce motor unit action potential. These motor unit action potentials travel through the muscle fibers with conduction velocity and superimposed signal gets recorded at the electrode site. Here we have taken an analogy from the speech production system model as the excitation signal travels through vocal tract to produce speech; similarly, an impulse train of firing rate frequency goes through the system with impulse response of motor unit action potentials and travels along the muscle fiber of that person. As the vocal tract contains the speaker information, we can also separate the muscle fiber pattern part and motor unit discharge pattern through proper selection of features and its classification to identify the respective person. Cepstral and non uniform filter bank features models the variation in the spectrum of the signals. Vector quantization and Gaussian mixture model are the two techniques of pattern matching have been applied.
Electron bunching in the optimal operating regime of a carcinotrodeVictor Solntsev
Electron bunching processes in a carcinotrode (backwardﱹwave oscillator with selfﱹmodulation of electron emission) operating in the highﱹefficiency regime determined previously are investigated. The posﱹ sibility of obtaining an efficiency of about 80% is explained from the physical viewpoint.
Dsp lab report- Analysis and classification of EMG signal using MATLAB.Nurhasanah Shafei
This document discusses a study analyzing and classifying electromyogram (EMG) signals. The researchers developed a MATLAB-based system that can differentiate EMG signals coming from different patients. The system analyzes time and frequency domain characteristics of the EMG signals, including median value, average value, root mean square, maximum power, and minimum power. It then uses these characteristics to identify which patient a given EMG signal belongs to through a graphical user interface. The system was able to accurately classify EMG signals from two patients based on their power spectrum signatures.
Nanostructured materials for magnetoelectronicsSpringer
This document discusses experimental approaches to studying magnetization and spin dynamics in magnetic systems with high spatial and temporal resolution.
It describes using time-resolved X-ray photoemission electron microscopy (TR-XPEEM) to image the temporal evolution of magnetization in magnetic thin films with picosecond time resolution. Results are presented showing the changing domain structure in a Permalloy thin film following excitation with a magnetic field pulse. Different rotation mechanisms are observed depending on the initial orientation of the magnetization with respect to the applied field.
A novel pump-probe magneto-optical Kerr effect technique using higher harmonic generation is also discussed for addressing spin dynamics in magnetic systems with femtosecond time resolution and element selectivity.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
The quality of data and the accuracy of energy generation forecast by artific...IJECEIAES
The paper presents the issues related to predicting the amount of energy generation, in a particular wind power plant comprising five generators located in south-eastern Poland. Thelocation of wind power plant, the distribution and type of applied generators, and topographical conditions were given and the correlation between selected weather parameters and the volume of energy generation was discussed. The primary objective of the paper was to select learning data and perform forecasts using artificial neural networks. For comparison, conservative forecasts were also presented. Forecasts results obtained shaw that Artificial Neural Networks are more universal than conservative method. However their forecast accuracy of forecasts strongly depends on the selection of explanatory data.
Development of Adaptive Neuro Fuzzy Inference System for Estimation of Evapot...ijsrd.com
The accuracy of an adaptive neurofuzzy computing technique in estimation of reference evapotranspiration (ETo) is investigated in this paper. The model is based on Adaptive Neurofuzzy Inference System (ANFIS) and uses commonly available weather information such as the daily climatic data, Maximum and Minimum Air Temperature, Relative Humidity, Wind Speed and Sunshine hours from station, Karjan (Latitude - 22°03'10.95"N, Longitude - 73°07'24.65"E), in Vadodara (Gujarat), are used as inputs to the neurofuzzy model to estimate ETo obtained using the FAO-56 Penman.Monteith equation. The daily meteorological data of two years from 2009 and 2010 at Karjan Takuka, Vadodara, are used to train the model, and the data in 2011 is used to predict the ETo in that year and to validate the model. The ETo in training period (Train- ETo) and the predicted results (Test-ETo) are compared with the ETo computed by Penman-Monteith method (PM-ETo) using "gDailyET" Software. The results indicate that the PM-ETo values are closely and linearly correlated with Train- ETo and Test- ETo with Root Mean Squared Error (RMSE) and showed the higher significances of the Train- ETo and Test- ETo. The results indict the feasibility of using the convenient model to resolve the problems of agriculture irrigation with intelligent algorithm, and more accurate weather forecast, appropriate membership function and suitable fuzzy rules.
Hybrid Algorithm for Dose Calculation in Cms Xio Treatment Planning SystemIOSR Journals
This study aimed at designing an improved hybrid algorithm by explicitly solving the linearized Boltzmann transport equation (LBTE) which is the governing equation that describes the macroscopic behaviour of radiation particles (neutrons, photons, electrons, etc). The algorithm accuracy will be evaluated using a newly designed in-house verification phantom and its results will be compared to those of the other XiO photon algorithms. The LBTE was solved numerically to compute photon transport in a medium. A programming code (algorithm) for the LBTE solution was developed and applied in the treatment planning system (TPS). The accuracy of the algorithm was evaluated by creating several plans for both the designed phantom and solid water phantom using the designed algorithm and other Xio photon algorithms. The plans were sent to a pre-calibrated Eleckta linear accelerator for measurement of absorbed dose.The results for all treatment plans using the hybrid algorithm compared to the 3 Xio photon algorithms were within 4 % limit. Calculation time for the hybrid algorithm was less in plans with larger number of beams compared to the other algorithms; however, it is higher for single beam plans. The hybrid algorithm provides comparable accuracy in treatment planning conditions to the other algorithms. This algorithm can therefore be employed in the calculation of dose in advance techniques such as IMRT and Rapid Arc by a radiotherapy centres with cmsxio treatment planning system as it is easy to implement.
Iaetsd recognition of emg based hand gesturesIaetsd Iaetsd
This document summarizes research on recognizing electromyography (EMG) signals from hand gestures to control prosthetics using artificial neural networks. EMG signals were collected from muscles during two hand gestures. Thirteen features were extracted from the signals and used to train and test several neural networks with different training algorithms. It was found that networks using the Levenberg-Marquardt algorithm achieved the best performance, with over 90% classification accuracy and the fastest training times, making it most suitable for accurate and rapid prosthetic control based on EMG pattern recognition.
Active vibration control of smart piezo cantilever beam using pid controllereSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Active vibration control of smart piezo cantilever beam using pid controllereSAT Journals
Abstract In this paper the modelling and Design of a Beam on which two Piezoelectric Ceramic Lead Zirconate Titanate ( PZT) patches are bonded on the top and bottom surface as Sensor/Actuator collocated pair is presented. The work considers the Active Vibration Control (AVC) using Proportional Integral Derivative (PID) Controller. The beam is assumed as Euler-Bernoulli beam. The two PZT patches are also treated as Euler-Bernoulli beam elements. The contribution of mass and stiffness of two PZT patches in the design of entire structure are also considered. The beam is modelled using three Finite Elements. The patches can be bonded near the fixed end, at middle or near the free end of the beam as collocated pair. The design uses first two dominant vibratory modes. The effect of PZT sensor/actuator pair is investigated at different locations of beam in vibration control. It can be concluded from the work that best result is obtained when the PZT patches are bonded near the fixed end. Keywords: Smart Beam, Active Vibration control, Piezoelectric, PID Controller, Finite Element
EMG Diagnosis using Neural Network Classifier with Time Domain and AR FeaturesIDES Editor
The shapes of motor unit action potentials
(MUAPs) in an electromyographic (EMG) signal
provide an important source of information for the
diagnosis of neuromuscular disorders. To extract this
information from the EMG signals, the first step is
identification of the MUAPs composed by the EMG
signal, second step is clustering of MUAPs with similar
shapes, third step is extraction of the features of MUAP
clusters and last step is classification of MUAPs. In this
work, the MUAPs are identified by using a data driven
segmentation algorithm, statistical pattern recognition
technique is used for clustering of MUAPs. Followed by
the extraction of time domain and autoregressive (AR)
features of the MUAP clusters. Finally, a neural
network (NN) classifier is used for classification of
MUAPs. A total of 12 EMG signals obtained from 3
normal (NOR), 5 myopathic (MYO) and 4 motor
neuron diseased (MND) subjects were analyzed. The
success rate for the segmentation technique is 95.90%
and for the statistical technique is 93.13%. The
classification accuracy of NN is 66.72% with time
domain parameters and 75.06 % with AR parameters.
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Learn more: https://www.brainlab.com/iplan-rt
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Bio-medical (EMG) Signal Analysis and Feature Extraction Using Wavelet TransformIJERA Editor
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Analysis of the Waveform of the Acoustic Emission Signal via Analogue Modulat...IOSRjournaljce
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This document discusses the use of surface electromyography (EMG) in biomechanics. It reviews many factors that can influence the EMG signal and force produced by a muscle, including intrinsic physiological factors like motor unit firing characteristics and extrinsic technical factors like electrode placement. The document focuses on three main applications of surface EMG: determining muscle activation timing, relating EMG to muscle force, and using EMG as a fatigue index. It provides recommendations for proper use and identifies outstanding challenges in the field.
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Evaluation of frequency domain features for myopathic emg signals in mat lab
1. Akash Kumar Bhoi et al. Int. Journal of Engineering Research and Application www.ijera.com
Vol. 3, Issue 5, Sep-Oct 2013, pp.622-627
www.ijera.com 622 | P a g e
Evaluation of Frequency Domain Features for Myopathic EMG
Signals in Mat Lab
Akash Kumar Bhoi1
, Devakishore Phurailatpam2
, Jitendra Singh Tamang3
1
Department of AE&I Engg, Sikkim Manipal Institute of Technology (SMIT), Majitar
2
Department of E&E Engg, National Institute of Technology, Manipur
3
Department of E&C Engg, Sikkim Manipal Institute of Technology (SMIT), Majitar
Abstract: The proposed EMG signals analysis relies on the frequency domain where features of healthy EMG
signal and myopathic EMG signals are analyzed and compared. Methodology described the relationship
between the EMG signals and the properties of a contracting & myopathic muscle by analysing its power
density spectrum. Periodogram Mean-Square Spectrum Estimate (PMSSE) of EMG Signal and the Power
spectral Density is calculated with Welch's PSD estimate method by taking Hamming & Kaiser Window for
both the healthy & myopathic signals. The analysis can provide important clues to design feature extraction
methods and the resulting information can be used to determine the origin of the weakness.
Keywords: EMG Signal, Myopathic Signal, power density spectrum, Welch’s PSD, PMSSE
I. Introduction
Clinical electromyography analyses the
electromyogram (EMG) recorded from a contracting
muscle using a needle electrode to diagnose
neuromuscular disorders. EMG is composed of
discrete waveforms called motor unit action
potentials (MUAPs), which result from the repetitive
discharges of groups of muscle fibers called motor
units (MUs). The term MU refers collectively to one
motoneuron and the group of muscle fibers it
innervates and is the smallest unit of skeletal muscle
that can be activated by volitional effort. MUAPs
from different MUs tend to have distinct shapes,
which remain almost the same for each discharge.
The MUAPs can therefore be identified and tracked
using pattern recognition techniques. The resulting
information can be used to determine the origin of the
weakness, i.e. neurogenic or myopathic diseases
[1,3]. The changes brought about by a particular
disease alter the properties of the muscle and nerve
cells, causing characteristic changes in the MUAPs.
Distinct MUAPs can be seen only during weak
contractions when few motor units are active. When a
patient maintains low level of muscle contraction,
individual MUAPs can be easily recognised. As
contraction intensity increases, more motor units are
recruited. Different MUAPs will overlap, causing an
interference pattern in which the neurophysiologist
cannot detect individual MUAP shapes reliably.
Usually, in clinical electromyography,
neurophysiologists assess MUAPs from their shape
using an oscilloscope and listening to their audio
characteristics. Thus, an experienced
electrophysiologist can detect abnormalities with
reasonable accuracy. However, subjective MUAP
assessment, although satisfactory for the detection of
unequivocal abnormalities, may not be sufficient to
delineate less obvious deviations or mixed patterns of
abnormalities [4]. Therefore, for an effective
automated MUAP assessment, a systematic handling
of EMG signal must decompose the signal into
MUAPs and classify each MUAP into different
classes.
Although, a number of computer-based
quantitative EMG analysis algorithms have been
developed [5] practically none of them has gained
wide acceptance for extensive clinical use. Most
importantly, there are no uniform international
criteria neither for pattern recognition of similar
MUAPs or for MUAP feature extraction [8]. Out of
the two assessment tasks (i.e. MUAP detection and
classification) according to our knowledge only the
first one has attracted attention. Buchthal et al. [9, 10]
developed one of the earliest methods for quantitative
EMG decomposition, where MUAPs were recorded
photographically and then were selected for analysis.
LeFever and DeLuca [11] used a special three
channel recording electrode and a visual computer
decomposition scheme based on template matching
and firing statistics for MUAP identification. Stalberg
et al. [8], in their original system used waveform
template matching whereas more recently [12] they
have used different shape parameters as input to a
template matching technique. Andreassen [13]
followed the manual method developed by Buchthal
using template matching with four templates for the
recognition of MUAP’s recorded at threshold
contraction. Stashuk and Qu [14] proposed a method
to identify MUAPs based on power spectrum
matching. Hassoun et al. [15] proposed a system
called neural network extraction of repetitive vectors
for electromyography (NNERVE) which uses the
time domain waveform as input to a three layer
RESEARCH ARTICLE OPEN ACCESS
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artificial neural network with a
‘‘pseudounsupervised’’ learning algorithm for
classification. McGill et al. [16] used a method based
on a combination of shape recognition of the MUAPs
and statistical probability of occurrence. Fang et al.
[17] developed a comprehensive technique to identify
single motor unit (SMU) potentials based on one-
channel EMG recordings measuring waveform
similarity of SMU potentials in the wavelet domain.
A common problem in signal and image
processing is in the rejection of signal noise
components and the increase of the signal-to-noise
ratio. This process involves the correct selection of
signal sampling frequency, frequency regions of
desired signal components and cut-off frequencies for
their extraction. In most cases the low-pass filter is
applied at first to reject signal noise and to reduce the
auto-aliasing effect followed by the high-pass filter to
remove signal trend components. The EMG signal is
nonstationary as its statistical properties change over
time. In most cases its sampling rate is greater than 1
kHz affecting possibilities of noise reduction. The
MUAPa are transients that exist for a short period of
time, which is not identical in all cases. For that
reason, time-frequency methods are now being used
to characterize the localized frequency content of
each MUAP [1]. Most often and even in our case,
wavelet transform is used.
II. Properties of the Power Density
Spectrum for EMG Signal
The power density spectrum of the EMG
signal may be formed by summing all the auto and
cross-spectra of the individual MUAPTs, as indicated
in this expression:
Where, Su, ( ) = the power density of the MU
APT, Ui (t); and S ( ) = the cross-power density
spectrum of MUAPTs Ui(t) and u;(t). This spectrum
will be nonzero if the firing rates of any two active
motor units are correlated. Finally, p = the total
number of MUAPTs that comprise the signal; q =
the number of MUAPTs with correlated discharges.
For details of this mathematical approach, refer to
De Luca and van Dyk (1975). De Luca et al (1982b)
have shown that many of the concurrently active
motor units have, during an isometric muscle
contraction, firing rates which are greatly correlated.
It is not yet possible to state that all concurrently
active motor units are correlated. Therefore, q is not
necessarily equal to p, which represents the total
number of MUAPTs in the EMG signal. The above
equation may be expanded to consider the following
facts:
1. During a sustained contraction, the
characteristics of the MUAP shape may change as
a function of time (r). For example, De Luca and
Forrest (1973a), Broman (1973, 1977), Kranz et al
(1983), and Mills (1982) have all reported an
increase in the time duration of the MUAP. [4][6]
2. The number of MUAPTs present in the EMG
signal will be dependent on the force of the
contraction (F).
3. The detected EMG signal will be filtered by the
electrode before it can be observed. This electrode
filtering function will be represented by R(w, d),
where d is the distance between the detection
surfaces of a bipolar electrode.
Note that the recruitment of motor units as a
function of time during a constant force has not been
considered; however, the required modification to the
equation is trivial, and the concept may easily be
accommodated. The concept of "motor unit rotation"
during a constant force contraction (i.e., newly
recruited motor units replacing previously active
motor units) which has, at times, been speculated to
exist, has also not been included. No account may be
found in the literature which has provided evidence
of this phenomenon by definitively excluding the
likelihood that the indwelling electrode has moved
relative to the active muscle fibers and, in fact,
records from a new motor unit territory in the muscle.
[1][8][10]
Where, MUAPT power density function
There are three eventualities that may
influence its time dependency: (1) the characteristics
of the shape of the MUAP Ui(t) and Uj(t) change as a
function of time; (2) the number of MUAPTs which
are correlated varies as a function of time; (3) the
degree of cross-correlation among the correlated
MUAPTs varies. A change in the shape of the MUAP
of Ui(t) and Uj(t) would not only cause an alteration
in the cross-power density term but also would cause
a more pronounced modification in the respective
auto power density spectra. Hence, the power density
spectrum of the EMG signal would be altered
regardless of the modifications of the individual
cross-power density spectra of the MUAPTs. There is
to date no direct evidence to support the other two
points. In fact, De Luca et al (1982a and b) have
presented data which indicate that the cross-
correlation of the firing rates of the concurrently
active motor units does not appear to depend on
either time during, or force of a contraction. [1][4]
This apparent lack of time-dependent cross-
correlation of the firing rates is not inconsistent with
previously mentioned observations, indicating that
the synchronization of the motor unit discharges
tends to increase with contraction time. These two
properties can be unrelated. Up to this point, the
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modeling approach has provided an explanation of
the following aspects and behavior of the power
density spectrum:
1. The amplitude increases with additionally
recruited MUAPTs.
2. The IPI firing statistics influence the shape of the
spectrum below 40 Hz, although this effect is not
necessarily consistent, and is less evident at
higher force when an increasing number of
motor units are active.
3. The tendency for motor units to "synchronize"
will affect the spectral characteristics but will be
limited to the low frequency components.
4. Modification in the waveform of MUAPs within
the duration of a train will affect most of the
spectrum of the EMG signal. This is particularly
worrisome in signals that are obtained during
contractions that are anisometric, because in such
cases the waveform of the MUAP may change in
response to the modification of the relative
distance between the active muscle fibers and the
detection electrode.
The above associations do not fully explain
the now well-documented property of the EMG
signal, which manifests itself as a shift towards the
low frequency end of the frequency spectrum during
sustained contractions. It is apparent that
modifications in the total spectral representation of
the MUAPs can only result from a modification in
the characteristics of the shape of the MUAP. During
attempted isometric contraction, such modifications
have their root cause in events that occur locally
within the muscle. Broman (1973) and De Luca and
Forrest (1973a) were the first to present evidence that
the MUAP increases in time duration during a
sustained contraction. [5] More recently, Kranz et al
(1981) and Mills (1982) have provided further
support. [6]
III. Spectral Analysis of EMG Signal
Spectrum analysis is also applied to EMG
studies. Various feature extraction methods based on
the spectral analysis are experimented. By using of
information contained in frequency domain could
lead to a better solution for encoding the EMG signal.
Time-frequency analysis based on short-time Fourier
transform is a form of local Fourier analysis that
treats time and frequency simultaneously and
systematically. The characters of EMG signals in
frequency domain are explored and demonstrated in
this chapter. The short time variability of spectrum,
which is an essential fact for using time-frequency
methods in EMG feature extraction, is also discussed
in this chapter. The analysis can provide important
clues to design feature extraction methods. Wavelets
approach is another powerful technique for time-
frequency analysis.
IV. Power Spectral Density (PSD) of
EMG Signal
EMG Signals cannot be described by a well-
defined formula. The distributions for the various
grasp types can be however described with the
probability laws. EMG signal is a random process
whose value at each time is a random variable. [7]
The Fourier transform we used in the previous
section views non random signals as weighted
integral of sinusoidal functions. Since a sample
function of random process can be viewed as being
selected from an ensemble of allowable time
functions, the weighting function for a random
process must refer in some way to the average rate of
change of the ensemble of allowable time functions.
The power spectral density (PSD) of a wide sense
stationary random process X (t) is computed from the
Fourier transform of the autocorrelation function R(τ
) :
Where the autocorrelation function
The nonparametric methods are methods in which the
estimate of PSD is made directly from a signal itself.
One type of such methods is called periodogram. The
periodogram estimate for PSD for discrete time
sequence x1, x2, x3 …. xk is defined as square
magnitude of the Fourier transform of data:
An improved nonparametric estimator of the
PSD is proposed by Welch P.D. The method consists
of dividing the time series data into (possibly
overlapping) segments, computing a modified
(windowed) periodogram of each segment, and then
averaging the PSD estimates. The result is Welch's
PSD estimate. The multitaper method (MTM) is also
a nonparametric PSD estimation technique which
uses multiple orthogonal windows.
The first step toward the computation of spectral
variables is the estimation of the PSD function of the
signal. When the voluntary myoelectric signal is
processed (albeit the raw periodogram is an
asymptotically unbiased but inconsistent spectral
estimator), both spectral variables (MNF and MOF)
are computed adding the amplitudes of many spectral
lines, thus dramatically reducing the effect of the in
determination of the power content of the individual
spectral lines.
V. Results
The EMG is collected from Physio Bank
ATM having 4000 samples of a healthy & Myopathic
subject where the length of the recorded signal was
10 seconds. The simulation part is carried out in Mat
lab platform.
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Fig 1. The input healthy/normal EMG signal
Fig 2. The input Myopathic EMG signal
1.1. Hamming window
The hamming window, w = hamming(L)
returns an L-point symmetric Hamming window in
the column vector w. L should be a positive integer.
The coefficients of a Hamming window are
computed from the following equation.
(n)=0.54-0.46
The window length is L=N+1
w = hamming (L,'sflag') returns an L-point Hamming
window using the window sampling specified by
'sflag', which can be either 'periodic' or 'symmetric'
(the default). The 'periodic' flag is useful for
DFT/FFT purposes, such as in spectral analysis. The
DFT/FFT contains an implicit periodic extension and
the periodic flag enables a signal windowed with a
periodic window to have perfect periodic extension.
When 'periodic' is specified, hamming computes a
length L+1 window and returns the first L points.
When using windows for filter design, the
'symmetric' flag should be used.
Fig 3. Power Spectral Density of Normal EMG
Signal with Hamming window
Fig 4. Power Spectral Density of Myopathic EMG
Signal with Hamming window
Fig 5. Periodogram Power Spectral Density Estimate
of Normal EMG Signal with Hamming Window
Fig 6. Periodogram Power Spectral Density Estimate
of Myopathic EMG Signal with Hamming Window
The power spectral density of the input
Normal EMG signal (fig-5) can be estimated by its
periodogram where the frequency range is from -500
to 500 Hz and the Power per frequency is -20 to +25
db/Hz in the both side-lobe of the spectrum but the
mid portion the Power per frequency (frequency -200
to +200 Hz) is about 0 to +40 db/Hz for Hamming
Window method.
Whereas the power spectral density of the
Myopathic EMG signal (fig-6) can be estimated by
its periodogram and the frequency range is from -500
to 500 Hz and the Power per frequency is -10 to +30
db/Hz in the both side-lobe of the spectrum but the
mid portion the Power per frequency (frequency -100
to +100 Hz) is about +10 to +40 db/Hz for Hamming
Window method.
1.2. Kaiser window
The Kaiser Window, w = Kaiser (L, beta)
returns an L-point Kaiser window in the column
vector w. beta is the Kaiser window β parameter that
affects the sidelobe attenuation of the Fourier
transform of the window. The default value for beta
is 0.5.To obtain a Kaiser window that designs an FIR
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filter with sidelobe attenuation of α dB, use the
following β.
Increasing beta widens the main lobe and decreases
the amplitude of the sidelobes (i.e., increases the
attenuation).
Fig 7. Power Spectral Density of Normal EMG
Signal with Kaiser Window
Fig 8. Power Spectral Density of Myopathic EMG
Signal with Kaiser Window
The Power Spectral Density of Normal EMG Signal
is found to be higher than that of myopathic signal. In
case of Hamming Window the max PSD is 44dB/Hz
for normal signal and 40 dB/Hz for Myopathic signal
whereas for Kaiser Window it is 45dB/Hz for normal
signal & 41dB/Hz for myopathic signal.
Fig 9. Periodogram Power Spectral Density Estimate
of normal EMG Signal with Kaiser Window
Fig 10. Periodogram Power Spectral Density
Estimate of Myopathic EMG Signal with Kaiser
Window
The Periodogram Power Spectral Density
Estimate of Normal EMG Signal with Kaiser
Window is being shown in Fig-9 where the Power
per frequency is decreasing from initial 80 dB/rad
sample to nearby 20 dB/rad sample gradually with
respect to its normalized frequency.
Whereas the Periodogram Power Spectral
Density Estimate of Myopathic EMG Signal with
Kaiser Window is being shown in Fig-10 and the
Power per frequency is decreasing from initial 70
dB/rad sample to nearby 20 dB/rad sample gradually
with respect to its normalized frequency
The Periodogarm Mean-Square Spectrum
estimate for the Normal EMG signal (fig-12) and its
frequency limits in the interval of -100 to 100 Hz and
the myopathic signal spectrum spreads over the
frequency range of -400 to 400 Hz.
During a sustained isometric contraction the
surface EMG signal becomes “slower”, the power
spectral density is compressed toward lower
frequencies and spectral variables (MNF, MDF)
decrease. The decrease of these variables reflects a
decrease of muscle fiber conduction velocity and
changes of other variables (such as active motor unit
pool, degree of synchronization, etc).
Fig11. Mean and median spectral frequencies of the
EMG signal (MNF and MDF)
Fig12. Periodogram Mean-Square Spectrum Estimate
of Normal EMG Signal
Fig13. Periodogram Mean-Square Spectrum Estimate
of Myopathic EMG Signal
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The PSD shown above summarizes the
frequency components for the entire length of the
EMG data. Another important part of spectral
analysis relies on studying how the frequency
components vary with time. Qualitative assessments
can be made by calculating the PSD for each segment
of data and comparing them.
VI. Conclusion
It has been shown that the mean and median
frequencies of the EMG signal decrease with time
during a task that induces fatigue. The result
essentially gives an evaluation of what contribution
each frequency has to the original signal. In order to
gain meaningful information from this type of
calculation, the segment of data being studied must
be stationary, meaning that the statistics of the signal
do not change with time. The most important
application of spectral analysis in our study was to
make differentiate between normal and myopathic
EMG signals and the spectral behaviour. Our analysis
leads to better investigation of myopathic diseases
and the origin of such diseases. Future research
involves the neuromuscular signal analysis and
disease findings.
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