A comparative study of wavelet families for electromyography signal classific...journalBEEI
Automatic detection of neuromuscular disorders performed using electromyography (EMG) has become an interesting domain for many researchers. In this paper, we present an approach to evaluate and classify the non-stationary EMG signals based on discrete wavelet transform (DWT). Most often researches did not consider the effect of DWT factors on the performance of EMG signals classification. This problem is still an interesting unsolved challenge. However, the selection of appropriate mother wavelet and related level decomposition is an essential issue that should be addressed in DWT-based EMG signals classification. The proposed method consists of decomposing a raw EMG signal into different sub-bands. Several statistical features were extracted from each sub-band and six wavelet families were investigated. The feature vector was used as inputs to support vector machine (SVM) classifier for the diagnosis of neuromuscular disorders. The obtained results achieve satisfactory performances with optimal DWT factors using 10-fold cross-validation. From the classification performances, it was found that sym14 is the most suitable mother wavelet at the 8th optimal wavelet level of decomposition. These simulation results demonstrated that the proposed method is very reliable for reducing cost computational time of automated neuromuscular disorders system and removing the redundancy information.
AN ELECTRICAL IMPEDANCE TOMOGRAPHY SYSTEM FOR THYROID GLAND WITH A TINY ELECT...ijbesjournal
Electrical impedance Tomography (EIT) is a non-invasive imaging technique based on measuring of the
electrical conductivity and capacitance of abnormal and normal human tissues. The present work aims to
develop an EIT imaging system for imaging thyroid gland. Patients with thyroid nodules were eligible for
the study. The study was conducted on two groups of participants: control group consists of 20 normal
female cases and experimental consists of 20 goiter female patients. The thyroid nodule location, size, and
type measured by ultrasound. Thyroid gland conductivity and permittivity were recorded using EIT. The
impedance measurement is done through the applying of two probes: one probe to the neck region
(scanning probe) and the rest region (reference probe) with electrolytic gel for each probe, then the system
software proceeds to reconstruct the image and calculate the electrical impedance of the thyroid gland on
a personal computer which acts as an output display and storage for case information. The thyroid
scanning probe has 64 electrodes embedded on a small space (30 mm diameter and 50 mm height) inside
of the probe. Multifrequency impedance measurements are typically made by applying an electric current
to a target mass by using of the scanning probe and measuring the developed voltage. The present EIT
system provides real- time visualization of the spatial distribution of the electrical properties of the thyroid
tissue. Images obtained from the bioimpedance (BI) were compared to images obtained from the
ultrasound imaging, results showed great similarity between the two diagnostic images. Tumor tissue has
higher resistance and capacitance value than that of normal thyroid gland.
METHODS FOR IMPROVING THE CLASSIFICATION ACCURACY OF BIOMEDICAL SIGNALS BASED...IAEME Publication
Biomedical signals are long records of electrical activity within the human body, and they faithfully represent the state of health of a person. Of the many biomedical signals, focus of this work is on Electro-encephalogram (EEG), Electro-cardiogram (ECG) and Electro-myogram (EMG). It is tiresome for physicians to visually examine the long records of biomedical signals to arrive at conclusions. Automated classification of these signals can largely assist the physicians in their diagnostic process. Classifying a biomedical signal is the process of attaching the signal to a disease state or healthy state. Classification Accuracy (CA) depends on the features extracted from the signal and the classification process involved. Certain critical information on the health of a person is usually hidden in the spectral content of the signal. In this paper, effort is made for the improvement in CA when spectral features are included in the classification process.
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.
Early detection of slight bruises in apples by cost-efficient near-infrared ...IJECEIAES
Near-infrared (NIR) spectroscopy has been widely reported for its useful applications in assessing internal fruit qualities. Motivated by apple consumption in the global market, this study aims to evaluate the possibility of applying NIR imaging to detect slight bruises in apple fruits. A simple optical setup was designed, and low-cost system components were used to promote the future development of practical and cost-efficient devices. To evaluate the effectiveness of the proposed approach, slight bruises were created by a mild impact with a comparably low impact energy of only 0.081 Joules. Experimental results showed that 100% of bruises in Jazz and Gala apples were accurately detected immediately after bruising and within 3 hours of storage. Thus, it is promising to develop customer devices to detect slight bruises for not only apple fruits but also other fruits with soft and thin skin at their early damage stages.
A comparative study of wavelet families for electromyography signal classific...journalBEEI
Automatic detection of neuromuscular disorders performed using electromyography (EMG) has become an interesting domain for many researchers. In this paper, we present an approach to evaluate and classify the non-stationary EMG signals based on discrete wavelet transform (DWT). Most often researches did not consider the effect of DWT factors on the performance of EMG signals classification. This problem is still an interesting unsolved challenge. However, the selection of appropriate mother wavelet and related level decomposition is an essential issue that should be addressed in DWT-based EMG signals classification. The proposed method consists of decomposing a raw EMG signal into different sub-bands. Several statistical features were extracted from each sub-band and six wavelet families were investigated. The feature vector was used as inputs to support vector machine (SVM) classifier for the diagnosis of neuromuscular disorders. The obtained results achieve satisfactory performances with optimal DWT factors using 10-fold cross-validation. From the classification performances, it was found that sym14 is the most suitable mother wavelet at the 8th optimal wavelet level of decomposition. These simulation results demonstrated that the proposed method is very reliable for reducing cost computational time of automated neuromuscular disorders system and removing the redundancy information.
AN ELECTRICAL IMPEDANCE TOMOGRAPHY SYSTEM FOR THYROID GLAND WITH A TINY ELECT...ijbesjournal
Electrical impedance Tomography (EIT) is a non-invasive imaging technique based on measuring of the
electrical conductivity and capacitance of abnormal and normal human tissues. The present work aims to
develop an EIT imaging system for imaging thyroid gland. Patients with thyroid nodules were eligible for
the study. The study was conducted on two groups of participants: control group consists of 20 normal
female cases and experimental consists of 20 goiter female patients. The thyroid nodule location, size, and
type measured by ultrasound. Thyroid gland conductivity and permittivity were recorded using EIT. The
impedance measurement is done through the applying of two probes: one probe to the neck region
(scanning probe) and the rest region (reference probe) with electrolytic gel for each probe, then the system
software proceeds to reconstruct the image and calculate the electrical impedance of the thyroid gland on
a personal computer which acts as an output display and storage for case information. The thyroid
scanning probe has 64 electrodes embedded on a small space (30 mm diameter and 50 mm height) inside
of the probe. Multifrequency impedance measurements are typically made by applying an electric current
to a target mass by using of the scanning probe and measuring the developed voltage. The present EIT
system provides real- time visualization of the spatial distribution of the electrical properties of the thyroid
tissue. Images obtained from the bioimpedance (BI) were compared to images obtained from the
ultrasound imaging, results showed great similarity between the two diagnostic images. Tumor tissue has
higher resistance and capacitance value than that of normal thyroid gland.
METHODS FOR IMPROVING THE CLASSIFICATION ACCURACY OF BIOMEDICAL SIGNALS BASED...IAEME Publication
Biomedical signals are long records of electrical activity within the human body, and they faithfully represent the state of health of a person. Of the many biomedical signals, focus of this work is on Electro-encephalogram (EEG), Electro-cardiogram (ECG) and Electro-myogram (EMG). It is tiresome for physicians to visually examine the long records of biomedical signals to arrive at conclusions. Automated classification of these signals can largely assist the physicians in their diagnostic process. Classifying a biomedical signal is the process of attaching the signal to a disease state or healthy state. Classification Accuracy (CA) depends on the features extracted from the signal and the classification process involved. Certain critical information on the health of a person is usually hidden in the spectral content of the signal. In this paper, effort is made for the improvement in CA when spectral features are included in the classification process.
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.
Early detection of slight bruises in apples by cost-efficient near-infrared ...IJECEIAES
Near-infrared (NIR) spectroscopy has been widely reported for its useful applications in assessing internal fruit qualities. Motivated by apple consumption in the global market, this study aims to evaluate the possibility of applying NIR imaging to detect slight bruises in apple fruits. A simple optical setup was designed, and low-cost system components were used to promote the future development of practical and cost-efficient devices. To evaluate the effectiveness of the proposed approach, slight bruises were created by a mild impact with a comparably low impact energy of only 0.081 Joules. Experimental results showed that 100% of bruises in Jazz and Gala apples were accurately detected immediately after bruising and within 3 hours of storage. Thus, it is promising to develop customer devices to detect slight bruises for not only apple fruits but also other fruits with soft and thin skin at their early damage stages.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Implementation of Radon Transformation for Electrical Impedance Tomography (E...ijistjournal
Radon Transformation is generally used to construct optical image (like CT image) from the projection data in biomedical imaging. In this paper, the concept of Radon Transformation is implemented to reconstruct Electrical Impedance Topographic Image (conductivity or resistivity distribution) of a circular subject. A parallel resistance model of a subject is proposed for Electrical Impedance Topography(EIT) or Magnetic Induction Tomography(MIT). A circular subject with embedded circular objects is segmented into equal width slices from different angles. For each angle, Conductance and Conductivity of each slice is calculated and stored in an array. A back projection method is used to generate a two-dimensional image from one-dimensional projections. As a back projection method, Inverse Radon Transformation is applied on the calculated conductance and conductivity to reconstruct two dimensional images. These images are compared to the target image. In the time of image reconstruction, different filters are used and these images are compared with each other and target image.
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.
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.
Personal identity verification based ECG biometric using non-fiducial features IJECEIAES
Biometrics was used as an automated and fast acceptable technology for human identification and it may be behavioral or physiological traits. Any biometric system based on identification or verification modes for human identity. The electrocardiogram (ECG) is considered as one of the physiological biometrics which impossible to mimic or stole. ECG feature extraction methods were performed using fiducial or non-fiducial approaches. This research presents an authentication ECG biometric system using non-fiducial features obtained by Discrete Wavelet Decomposition and the Euclidean Distance technique was used to implement the identity verification. From the obtained results, the proposed system accuracy is 96.66% also, using the verification system is preferred for a large number of individuals as it takes less time to get the decision.
Moving One Dimensional Cursor Using Extracted ParameterCSCJournals
This study focuses on developing a method to determine parameters to control cursor movement using noninvasive brain signals, or electroencephalogram (EEG) for brain-computer interface (BCI). There were two conditions applied i.e. Control condition where subjects relax (resting state); and Task condition where subjects imagine a movement. During both conditions, EEG signals were recorded from 19 scalp locations. In Task condition, subjects were asked to imagine a movement to move the cursor on the screen towards target position. Fast Fourier Transform (FFT) was used to analyse the recorded EEG signals. To obtain maximum speed and accuracy, EEG data were divided into various interval and difference in power values between Task and Control conditions were calculated. As conclusion, the present study suggests that difference in delta frequency band between resting and active imagination may be use to control one dimensional cursor movement and the region that gives optimum output is at the parietal region.
Hand motion pattern recognition analysis of forearm muscle using MMG signalsjournalBEEI
Surface Mechanomyography (MMG) is the recording of mechanical activity of muscle tissue. MMG measures the mechanical signal (vibration of muscle) that generated from the muscles during contraction or relaxation action. It is widely used in various fields such as medical diagnosis, rehabilitation purpose and engineering applications. The main purpose of this research is to identify the hand gesture movement via VMG sensor (TSD250A) and classify them using Linear Discriminant Analysis (LDA). There are four channels MMG signal placed into adjacent muscles which PL-FCU and ED-ECU. The features used to feed the classifier to determine accuracy are mean absolute value, standard deviation, variance and root mean square. Most of subjects gave similar range of MMG signal of extraction values because of the adjacent muscle. The average accuracy of LDA is approximately 87.50% for the eight subjects. The finding of the result shows, MMG signal of adjacent muscle can affect the classification accuracy of the classifier.
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.
Effect of Endurance on Gastrocnemius Muscle with Exercise by Employing EMG Am...ijtsrd
Muscle fatigue is a common experience in daily life. Many authors have defined it as the incapacity to maintain the required or expected force, and therefore, force, power and torque recordings have been used as direct measurements of muscle fatigue. In addition, the measurement of these variables combined with the measurement of surface electromyography sEMG recordings which can be measured during all types of movements during exercise may be useful to assess and understand muscle fatigue. EMG signal can be easily analyzed in time domain, frequency domain and time frequency domain. The time domain features are the most popular in EMG pattern recognition because they are easy and quick to calculate and they do not require a transformation. The purpose of this study was to analyze the fatigue and to study the endurance occurrence in the Gastrocnemius muscle with a pre defined exercise protocol for the targeted muscle. For this purpose, sEMG Amplitude parameters were characterized. Relation between EMG features like mean, force, standard deviation, etc. is verified for fatigue detection as well as to identify the Endurance developed in the Gastrocnemius muscle. Gaurav Patti | Poonam Kumari "Effect of Endurance on Gastrocnemius Muscle with Exercise by Employing EMG Amplitude Parameters" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33222.pdf Paper Url :https://www.ijtsrd.com/engineering/other/33222/effect-of-endurance-on-gastrocnemius-muscle-with-exercise-by-employing-emg-amplitude-parameters/gaurav-patti
Segmentation and Classification of Brain MRI Images Using Improved Logismos-B...IJERA Editor
Automated reconstruction and diagnosis of brain MRI images is one of the most challenging problems in medical imaging. Accurate segmentation of MRI images is a key step in contouring during radiotherapy analysis. Computed tomography (CT) and Magnetic resonance (MR) imaging are the most widely used radiographic techniques in diagnosis and treatment planning. Segmentation techniques used for the brain Magnetic Resonance Imaging (MRI) is one of the methods used by the radiographer to detect any abnormality specifically in brain. The method also identifies important regions in brain such as white matter (WM), gray matter (GM) and cerebrospinal fluid spaces (CSF). These regions are significant for physician or radiographer to analyze and diagnose the disease. We propose a novel clustering algorithm, improved LOGISMOS-B to classify tissue regions based on probabilistic tissue classification, generalized gradient vector flows with cost and distance function. The LOGISMOS graph segmentation framework. Expand the framework to allow regionally-aware graph construction and segmentation.
Dielectrophoresis-based microfluidic device for separation of potential cance...journalBEEI
Cancer is a leading cause of death that adversely affects all ages and genders around the world. There is a range of approaches such as CT scanning and mammography to diagnose cancer. Although the current method has many benefits, most of it share similar drawbacks as the result of detection takes long time and can lead to over diagnosis. Dielectrophoresis (DEP) is a method that can be used to obtain the cell electrical properties such as capacitance, conductivity, and permittivity. A device was designed in this study using a pair of electrodes and main channel with two inlets and two outlets. COMSOL software is adopted to analyze channel particle flow. Results show the configuration of microfluidic device and its dimensions. For potential application, DEP may be used as a non-invasive technique to distinguish normal cell from cancerous cell, which can lead to early detection as it offers a real-time warning. The simulations reveal that the electrodes captured the particles successfully and sorted them within specific time. The chance of cell capture and the ability of the electrodes to sort the cells is around 80%. In addition, an ideal design of the microfluidic chip was established, incorporating the cell and dielectric properties.
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
Survey analysis for optimization algorithms applied to electroencephalogramIJECEIAES
This paper presents a survey for optimization approaches that analyze and classify electroencephalogram (EEG) signals. The automatic analysis of EEG presents a significant challenge due to the high-dimensional data volume. Optimization algorithms seek to achieve better accuracy by selecting practical features and reducing unwanted features. Forty-seven reputable research papers are provided in this work, emphasizing the developed and executed techniques divided into seven groups based on the applied optimization algorithm particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), grey wolf optimizer (GWO), Bat, Firefly, and other optimizer approaches). The main measures to analyze this paper are accuracy, precision, recall, and F1-score assessment. Several datasets have been utilized in the included papers like EEG Bonn University, CHB-MIT, electrocardiography (ECG) dataset, and other datasets. The results have proven that the PSO and GWO algorithms have achieved the highest accuracy rate of around 99% compared with other techniques.
A Novel Approach for Measuring Electrical Impedance Tomography for Local Tiss...CSCJournals
This paper proposes a novel approach for measuring Electrical Impedance Tomography (EIT) of a living tissue in a human body. EIT is a non-invasive technique to measure two or three-dimensional impedance for medical diagnosis involving several diseases. To measure the impedance value electrodes are connected to the skin of the patient and an image of the conductivity or permittivity of living tissue is deduced from surface electrodes. The determination of local impedance parameters can be carried out using an equivalent circuit model. However, the estimation of inner tissue impedance distribution using impedance measurements on a global tissue from various directions is an inverse problem. Hence it is necessary to solve the inverse problem of calculating mathematical values for current and potential from conducting surfaces. This paper proposes a novel algorithm that can be successfully used for estimating parameters. The proposed novel hybrid model is a combination of an artificial intelligence based gradient free optimization technique and numerical integration. This ameliorates the achievement of spatial resolution of equivalent circuit model to the closest accuracy. We address the issue of initial parameter estimation and spatial resolution accuracy of an electrode structure by using an arrangement called “divided electrode” for measurement of bio-impedance in a cross section of a local tissue.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Implementation of Radon Transformation for Electrical Impedance Tomography (E...ijistjournal
Radon Transformation is generally used to construct optical image (like CT image) from the projection data in biomedical imaging. In this paper, the concept of Radon Transformation is implemented to reconstruct Electrical Impedance Topographic Image (conductivity or resistivity distribution) of a circular subject. A parallel resistance model of a subject is proposed for Electrical Impedance Topography(EIT) or Magnetic Induction Tomography(MIT). A circular subject with embedded circular objects is segmented into equal width slices from different angles. For each angle, Conductance and Conductivity of each slice is calculated and stored in an array. A back projection method is used to generate a two-dimensional image from one-dimensional projections. As a back projection method, Inverse Radon Transformation is applied on the calculated conductance and conductivity to reconstruct two dimensional images. These images are compared to the target image. In the time of image reconstruction, different filters are used and these images are compared with each other and target image.
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.
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.
Personal identity verification based ECG biometric using non-fiducial features IJECEIAES
Biometrics was used as an automated and fast acceptable technology for human identification and it may be behavioral or physiological traits. Any biometric system based on identification or verification modes for human identity. The electrocardiogram (ECG) is considered as one of the physiological biometrics which impossible to mimic or stole. ECG feature extraction methods were performed using fiducial or non-fiducial approaches. This research presents an authentication ECG biometric system using non-fiducial features obtained by Discrete Wavelet Decomposition and the Euclidean Distance technique was used to implement the identity verification. From the obtained results, the proposed system accuracy is 96.66% also, using the verification system is preferred for a large number of individuals as it takes less time to get the decision.
Moving One Dimensional Cursor Using Extracted ParameterCSCJournals
This study focuses on developing a method to determine parameters to control cursor movement using noninvasive brain signals, or electroencephalogram (EEG) for brain-computer interface (BCI). There were two conditions applied i.e. Control condition where subjects relax (resting state); and Task condition where subjects imagine a movement. During both conditions, EEG signals were recorded from 19 scalp locations. In Task condition, subjects were asked to imagine a movement to move the cursor on the screen towards target position. Fast Fourier Transform (FFT) was used to analyse the recorded EEG signals. To obtain maximum speed and accuracy, EEG data were divided into various interval and difference in power values between Task and Control conditions were calculated. As conclusion, the present study suggests that difference in delta frequency band between resting and active imagination may be use to control one dimensional cursor movement and the region that gives optimum output is at the parietal region.
Hand motion pattern recognition analysis of forearm muscle using MMG signalsjournalBEEI
Surface Mechanomyography (MMG) is the recording of mechanical activity of muscle tissue. MMG measures the mechanical signal (vibration of muscle) that generated from the muscles during contraction or relaxation action. It is widely used in various fields such as medical diagnosis, rehabilitation purpose and engineering applications. The main purpose of this research is to identify the hand gesture movement via VMG sensor (TSD250A) and classify them using Linear Discriminant Analysis (LDA). There are four channels MMG signal placed into adjacent muscles which PL-FCU and ED-ECU. The features used to feed the classifier to determine accuracy are mean absolute value, standard deviation, variance and root mean square. Most of subjects gave similar range of MMG signal of extraction values because of the adjacent muscle. The average accuracy of LDA is approximately 87.50% for the eight subjects. The finding of the result shows, MMG signal of adjacent muscle can affect the classification accuracy of the classifier.
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.
Effect of Endurance on Gastrocnemius Muscle with Exercise by Employing EMG Am...ijtsrd
Muscle fatigue is a common experience in daily life. Many authors have defined it as the incapacity to maintain the required or expected force, and therefore, force, power and torque recordings have been used as direct measurements of muscle fatigue. In addition, the measurement of these variables combined with the measurement of surface electromyography sEMG recordings which can be measured during all types of movements during exercise may be useful to assess and understand muscle fatigue. EMG signal can be easily analyzed in time domain, frequency domain and time frequency domain. The time domain features are the most popular in EMG pattern recognition because they are easy and quick to calculate and they do not require a transformation. The purpose of this study was to analyze the fatigue and to study the endurance occurrence in the Gastrocnemius muscle with a pre defined exercise protocol for the targeted muscle. For this purpose, sEMG Amplitude parameters were characterized. Relation between EMG features like mean, force, standard deviation, etc. is verified for fatigue detection as well as to identify the Endurance developed in the Gastrocnemius muscle. Gaurav Patti | Poonam Kumari "Effect of Endurance on Gastrocnemius Muscle with Exercise by Employing EMG Amplitude Parameters" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33222.pdf Paper Url :https://www.ijtsrd.com/engineering/other/33222/effect-of-endurance-on-gastrocnemius-muscle-with-exercise-by-employing-emg-amplitude-parameters/gaurav-patti
Segmentation and Classification of Brain MRI Images Using Improved Logismos-B...IJERA Editor
Automated reconstruction and diagnosis of brain MRI images is one of the most challenging problems in medical imaging. Accurate segmentation of MRI images is a key step in contouring during radiotherapy analysis. Computed tomography (CT) and Magnetic resonance (MR) imaging are the most widely used radiographic techniques in diagnosis and treatment planning. Segmentation techniques used for the brain Magnetic Resonance Imaging (MRI) is one of the methods used by the radiographer to detect any abnormality specifically in brain. The method also identifies important regions in brain such as white matter (WM), gray matter (GM) and cerebrospinal fluid spaces (CSF). These regions are significant for physician or radiographer to analyze and diagnose the disease. We propose a novel clustering algorithm, improved LOGISMOS-B to classify tissue regions based on probabilistic tissue classification, generalized gradient vector flows with cost and distance function. The LOGISMOS graph segmentation framework. Expand the framework to allow regionally-aware graph construction and segmentation.
Dielectrophoresis-based microfluidic device for separation of potential cance...journalBEEI
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2. Typically, EMG data collection guidelines aim to in-
crease signal amplitude while minimizing noise, thus in-
creasing the signal-to-noise ratio [10]. The goal of
finding a high amplitude signal was also reported by
Zipp [9] who wrote that electrode placement should
meet three criteria: 1) repeatability, 2) consideration of
individual body dimensions, and 3) a high signal yield (i.
e., high amplitude). Higher amplitude signals could be
used to identify the area of the muscle with the most ac-
tivity. In addition, maximum mean and median power
spectral frequencies (MNF and MDF, respectively) are
associated with areas near the IZ [6]. In the IZ, action
potentials are incomplete or non-propagating, therefore
shorter in duration and lower in amplitude than propa-
gating action potentials [12]. The action potentials also
tend to move in opposite directions away from the IZ,
resulting in signals that are similar in shape but opposite
in phase [13,14]. Due to differential amplification, signal
amplitude over the IZ will be substantially reduced [6].
The work reported in this paper is a part of an ongoing
research effort that is exploring the use of an electrode
array to simplify electrode placement. Instead of
attempting to place a single electrode pair optimally,
which is a time-consuming process, an electrode array is
placed on top of the muscle of interest and an automated
method is used to select an optimal electrode pair based
on sEMG signal characteristics. The electrode array could
be integrated in a wearable sleeve to enable quick place-
ment of the array. The objective of this study is to evaluate
commonly used sEMG parameters for assessing the quality
of electrode placement, as an initial step towards develop-
ing a method to automatically select an optimal electrode
pair from an electrode array. Parameters are evaluated
based on their repeatability across trials, demonstrating
parameter consistency. The appropriateness of this ap-
proach will also be examined by comparing the proposed
methods of electrode selection to the traditional method
of electrode placement using only anatomical landmarks.
Methods
Subjects
A convenience sample of eight individuals, six males and
two females, was recruited from the University of New
Brunswick. The average participant characteristics were:
age = 27.6 years (sd = 7.3), weight = 81.0 kg (sd = 23.5),
height = 174.6 cm (sd = 10.0). The Research Ethics Board
of the University of New Brunswick approved the experi-
mental procedure used for this research and each subject
provided informed consent prior to data collection.
Data collection
Data were collected in the Biosignals Laboratory at the In-
stitute of Biomedical Engineering, University of New
Brunswick. A REFA multi-channel amplifier system (TMS
International) and proprietary software (PortiLab2) was
used for sEMG data acquisition. The REFA system specifi-
cations are listed in Table 1. Two-millimeter diameter
recessed sintered Ag-AgCl disc electrodes with driven
shielded cable were filled with electrode gel, attached to
the skin using double-sided tape, and used to simultan-
eously collect 84 channels of monopolar EMG data
(Figure 1).The REFA system amplifier gains were set at
20x and the system incorporates anti-alias filters with
6.786 kHz cutoff frequencies, samples the inputs at
131 kHz and then passes the sampled signals through 5th
order SINC filters with 26.7 kHz cutoffs. The collected
data were then post-processed with the PortiLab software
to be low-pass filtered at 500 Hz with a first-order digital
IIR filter, high-pass filtered at 10 Hz with a first-order
digital IIR filter and down-sampled to 2048 Hz.
For each subject, the skin was cleaned with alcohol
pads prior to electrode placement. Due to the small elec-
trode size, hair was not an issue so the skin was not
shaved. Using the SENIAM electrode placement guide-
lines [5] the recommended electrode position was
located for seven muscles and marked with an "x" on the
skin. The muscles included: tibialis anterior (TA), gastro-
cnemius medialis (GM), gastrocnemius lateralis (GL),
vastus lateralis (VL), rectus femoris (RF), biceps femoris
(BF), and semitendinosus (ST). As shown in Figure 2a, a
grid was centered on the "x", and ink dots were made on
the skin (Figure 2b). An electrode was placed on each
dot to form a 3 × 4 electrode array over each muscle
(Figure 2c). The array was positioned such that potential
electrode pairs were oriented parallel to the direction of
the muscle fibres.
Four tasks were chosen to target specific muscles
(Table 2). For each trial, five seconds of data were col-
lected during a maximal isometric contraction. To en-
sure that all electrodes were functioning properly
throughout data collection, a resting trial was recorded
before and after data collection. Subjects were permitted
a rest period between trials to minimize any fatigue
effect.
Table 1 REFA specifications (TMS International)
Parameter Specification
Input Impedance > 1012
CMRR > 100 dB
Gain 20 V/V
Noise 1.0μVrms
Anti-Alias Filter 1st order RC filter, cutoff frequency = 6.786KHz
Low Pass Digital
Filter
5th order SINC filter, cutoff frequency = 0.2035 *
sample frequency
Analog to Digital
Converter
22 bit resolution sigma delta
Kendell et al. Journal of NeuroEngineering and Rehabilitation 2012, 9:24 Page 2 of 8
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3. Data analysis
Each array contained 12 electrodes. Since monopolar
electrodes were used, nine electrode pairs were defined
(Figure 2c). The difference in voltage was calculated to
yield the bipolar EMG signal for each pair.
A total of 280 trials (8 subjects × 7 muscles/subject× 5
trials/muscle) were analyzed using Matlab (Mathworks,
Natick MA). For each trial, 5 s of data were used to calcu-
late twelve parameters. Six of the parameters were time
domain parameters: root mean square (RMS), mean abso-
lute value (MAV), maximum amplitude (MAX), slope sign
changes (SSC) [15], zero crossing (ZC) [15], and waveform
length (WL) [15]. Time domain parameters were extracted
from the raw EMG data, except for MAX. The MAX value
corresponded to the maximum amplitude of the EMG en-
velope, obtained by low pass filtering the fully rectified
EMG signal with a 6th
order Butterworth filter with a cut-
off frequency of 5 Hz.
The remaining six parameters were spectral para-
meters: mean frequency (MNF) [16], median frequency
(MDF) [17], signal-to-motion artifact ratio (SMR), max-
imum-to-minimum drop in power density ratio (DPR),
signal-to-noise ratio (SNR), and the power spectrum de-
formation (Ω); the latter four spectral parameters were
used previously to quantify EMG signal quality [18] and
are described in further detail in Appendix A. The EMG
power spectral density (PSD) was computed via Welch’s
averaged modified periodogram, with 1024 sample ana-
lysis windows, overlapping by 50%.
For all trials and all parameters, each pair within an
array was ranked based on the value for each parameter,
with 1 being assigned to the electrode pair with the best
value for that parameter and 9 being assigned to the pair
with the worst value. MNF and MDF were found to be
higher near the IZ [6], so a rank of 1 was assigned to the
electrode pair with the lowest value. Similarly, a rank of
1 was assigned for the electrode pair with the lowest
power spectrum deformation (Ω). For all other para-
meters, the electrode pairs with the maximum values
were ranked as 1.
Repeatability
A parameter was considered repeatable, for a particular
muscle and a particular subject, if an electrode pair had
a consistent ranking of 1. For each parameter, for all sub-
jects and all muscles, the number of times that the
Figure 1 The application of electrode arrays.
Figure 2 Array placement and electrode pair formation. a) The
grid used to mark electrode locations; (b) Marks made on the skin
using the grid. The "x" denotes the guideline-recommended
electrode location, while the dots are used to mark where electrodes
are placed; (c) The formation of 9 bipolar electrode pairs from 12
monopolar electrodes.
Kendell et al. Journal of NeuroEngineering and Rehabilitation 2012, 9:24 Page 3 of 8
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4. parameter met the repeatability criteria was counted.
The repeatability-4 score is the number of electrode pairs
that had a ranking of 1 for at least 4 of 5 trials. The re-
peatability-5 score is the number of electrode pairs that
had a ranking of 1 for all 5 trials. A percentage value was
calculated based on how many times the condition of re-
peatability was met out of the 56 possible cases (8 sub-
jects × 7 muscles).
Inter-parameter agreement
For each subject and muscle, all parameters that were
identified as repeatable, using the repeatability-4 criteria,
were compared against all the other parameters for each
trial. If another parameter had the same electrode pair,
with a ranking of 1 for at least 3 of the 5 trials, there was
inter-parameter agreement between these parameters for
that subject and muscle. Inter-parameter agreement was
computed between all parameters, resulting in a 12 × 12
inter-parameter agreement matrix. The value on the di-
agonal of the matrix corresponds to a parameter com-
pared against itself, and therefore should equal the
repeatability value of that parameter.
Agreement
If the electrode pair corresponding to the guideline-
recommended electrode position (hereafter referred to as
the guideline-recommend electrode pair) was the same
as the electrode pair with a ranking of 1, electrode place-
ment was considered to be in agreement. For each sub-
ject and muscle (8 subjects × 7 muscles = 56 cases), the
case was considered in agreement if the guideline-
recommended electrode pair and the top ranked pair
were the same for 4 out of 5 trials.
Results
Repeatability
Table 3 shows the repeatability of each parameter. The
parameter with the highest repeatability-4 was RMS
amplitude (82.14%) and the least repeatable parameter
was SMR (7.14%). For the time domain parameters, RMS
(82.14%), MAV (78.57%), and WL (80.36%) had the high-
est repeatability-4 values. For the spectral parameters,
MNF (75.00%) and MDF (75.00%) had highest repeat-
ability-4 values, which were slightly lower than the high-
est time domain parameters.
Repeatability-5 has a stricter requirement than repeat-
ability-4, and Table 3 shows an expected decrease in all
values from repeatability-4 to repeatability-5. The highest
valued repeatability-4 time domain parameters (RMS,
MAV, WL) did not decrease as much as the highest
valued repeatability-4 frequency domain parameters
(MNF, MDF).
Inter-parameter agreement
Table 4 shows the inter-parameter agreement matrix.
There was perfect inter-parameter agreement between
RMS and MAV and strong inter-parameter agreement
between MAX and both RMS and MAV. Although the
WL repeatability value was high, inter-parameter agree-
ment was not as strong as RMS, MAV, and MAX. MNF
and MDF also had high repeatability values and appear
to have inter-parameter agreement with each other, al-
though not a strong agreement.
Agreement
The percentage agreement between each parameter and
the guideline-recommended electrode pair is presented
in Table 5. Agreement for all parameters was low, with a
maximum of 16.07% for WL. When all parameters were
considered, the average agreement was only 8.48%.
Discussion
Selecting the best sEMG channel based on amplitude
parameters is straightforward in principle; the channel/
pair with the highest amplitude are due to electrodes
being placed over the most active muscle area. Since
Table 2 Description of tasks performed by subjects and the muscles targeted
Task Description Muscle(s)
Resisted Plantarflexion Subjects gripped the edge of a heavily weighted table (approximately 200 Kg). Subjects were asked to
generate a maximal contraction by plantarflexing against the weight of the table (i.e., attempting to lift the
table using only plantarflexion).
GM
GL
Resisted Dorsiflexion Subjects stood with both feet on the ground. A research assistant used both hands to push downward against
the dorsal surface of the foot. Subjects were asked to generate a maximal contraction by dorsiflexing against
the resistance provided by the research assistant.
TA
Resisted Knee Extension Subjects stood on one leg with the contralateral knee flexed to 90 °. A research assistant sat behind the subject
and held the foot to maintain knee flexion. The subject was then asked to generate a maximal contraction by
attempting to extend the knee against the resistance provided by the research assistant.
RF
VL
Resisted Knee Flexion Subjects stood on one leg with the contralateral knee flexed to 90 °. A research assistant sat behind the subject
and placed both hands over the heel of the flexed limb. The subject was asked to generate a maximal
contraction by attempting to flex the knee against the resistance provided by the research assistant.
BF
ST
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5. amplitude is lower over the IZ, choosing the best pair
based on maximum amplitude parameters would reduce
the possibility of choosing an electrode pair over the IZ.
Also, by increasing amplitude, the signal-to-noise ratio is
effectively increased.
For sEMG data collection, repeatability of the measured
signal given common measurement conditions is essential.
Based on repeatability between five trials, across all sub-
jects and lower extremity muscles, six parameters had re-
peatability-4 values greater than 75%. From the highest
repeatability value to lowest, these six parameters were
RMS, WL, MAV, MNF, MDF, and MAX. Only RMS and
MAV had a repeatability-5 value greater than 70%, sug-
gesting that these parameters are more robust.
Good inter-parameter agreement was found between
RMS and MAV, which is expected since they are both pro-
portional to the average EMG amplitude (RMS corresponds
to square root of the average square EMG magnitude and
MAV corresponds to the average absolute magnitude).
There was also good inter-parameter agreement between
these parameters and the MAX parameter; however, the
agreement was weaker than RMS-MAV. Since the MAX
parameter corresponds to a single value over the EMG sig-
nal, rather than an average, its value is susceptible to higher
variability and would be expected to be less reliable. The
lower repeatability value associated with MAX reinforces
this statement. Also, the WL parameter has only a modest
inter-parameter agreement with RMS, MAV, and MAX,
despite these parameters all having good repeatability
values. WL is associated with the EMG signal derivative,
computed as the sum of the absolute difference between ad-
jacent samples. This single parameter provides a measure of
waveform amplitude, frequency, and duration [15]. Its sensi-
tivity to frequency content may explain why WL only had a
Table 4 Inter-parameter agreement matrix
Parameter RMS MAV MAX SSC ZC WL MNF MDF SMR DPR SNR Ω
Root mean square (RMS) 46 46 44 0 0 28 13 7 7 21 25 9
Mean absolute value (MAV) 44 44 42 0 0 27 13 8 7 22 25 9
Maximum amplitude (MAX) 38 39 39 0 0 24 12 7 4 18 23 7
Slope sign changes (SSC) 0 0 0 31 20 4 0 1 2 0 0 0
Zero crossings (ZC) 0 0 0 19 29 7 0 0 3 0 0 5
Waveform length (WL) 30 27 27 6 6 45 5 2 8 12 13 8
Mean Frequency (MNF) 13 14 13 0 0 5 42 30 5 20 17 5
Median Frequency (MDF) 6 7 7 2 1 1 34 42 4 13 11 1
Signal-to-motion artifact ratio (SMR) 2 3 3 1 1 2 0 0 4 2 2 1
Maximum-to-minimum drop in power density ratio (DPR) 12 14 14 0 0 5 12 9 4 26 25 8
Signal-to-noise ratio (SNR) 20 21 19 0 0 11 14 10 6 30 36 12
Power spectrum deformation (Ω) 8 7 7 2 5 8 1 0 5 7 10 30
Each row considers cases that are considered repeatable based on the repeatability-4 criteria for a given parameter. The values indicate how many of these cases
had a ranking of 1, for the same electrode pair, in at least 3 of the 5 trials for another parameter (column).
Table 3 Repeatability for each EMG parameter
Parameter Repeatability-4*
Repeatability-5†
of 56 cases % of 56 cases %
Root mean square (RMS) 46 82.14 40 71.43
Mean absolute value (MAV) 44 78.57 40 71.43
Maximum amplitude (MAX) 39 69.64 29 51.79
Slope sign changes (SSC) 31 55.36 21 37.50
Zero crossings (ZC) 29 51.79 18 32.14
Waveform length (WL) 45 80.36 38 67.86
Mean Frequency (MNF) 42 75.00 24 42.86
Median Frequency (MDF) 42 75.00 21 37.5
Signal-to-motion artifact ratio (SMR) 4 7.14 1 1.79
Maximum-to-minimum drop in power density ratio (DPR) 26 46.43 17 30.36
Signal-to-noise ratio (SNR) 36 64.29 21 37.50
Power spectrum deformation (Ω) 30 53.57 20 35.71
*
number of cases where the top ranked (i.e., ranked 1) electrode pair was the same for at least 4/5 trials
†
number of cases where the top ranked electrode pair was the same for all 5 trials.
Kendell et al. Journal of NeuroEngineering and Rehabilitation 2012, 9:24 Page 5 of 8
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6. modest inter-parameter agreement with the amplitude
measures.
The spectral parameters associated with EMG signal
quality (i.e., SMR, DPR, SNR, and Ω) exhibited low repeat-
ability. The EMG data used in this study were from iso-
metric contractions in a laboratory setup; therefore, EMG
signal quality was expected to be quite high in all cases.
Indeed, the mean ± standard deviation of these parameters
across all cases were: SMR 26.25 ± 12.46 dB, DPR
42.25 ± 7.19 dB, SNR 30.99 ± 5.57 dB, and Ω 0.73± 0.17.
While these parameters do not appear to be beneficial for
identifying an optimal electrode pair, they could be useful
for excluding EMG channels with a low signal quality.
There is a correlation between amplitude and fre-
quency content as a function of electrode position, with
EMG from the IZ corresponding to a minimum in amp-
litude and maximum in MNF and MDF [6]; however,
our results showed a low inter-parameter agreement be-
tween amplitude parameters (RMS, MAV, and MAX)
and the MNF and MDF spectral parameters. These spec-
tral parameters may have been more sensitive to elec-
trode position along the muscle fiber axis than the
orthogonal direction. Repeatability values of the spectral
parameters decreased much more than the time domain
parameters from repeatability-4 to repeatability-5. This
suggests that time domain parameters would provide a
more consistent method for electrode selection. Larger
differences were observed with amplitude parameters
than spectral parameters as a function of distance from
the IZ [6], which also suggests that amplitude parameters
can be expected to be more repeatable.
It is important to note that muscle geometry is an im-
portant factor in EMG [19-21]. For example, muscle
fibers may also have inhomogeneities due to various fiber
orientations [21]. In the context of this work, the influ-
ence of the IZ on surface EMG may not be as prevalent
in pinnate muscles (e.g., GM, GL, VL, and RF), where
muscle fibers insert at an oblique angle to the tendon,
compared to fusiform muscles (e.g., TA, BF, and ST);
however, IZ effects are still observable in surface EMG
measurements [11].
Agreement was examined to determine whether the
electrode pair placed over the guideline-recommended lo-
cation was the same as the top ranked pair for any of the
twelve parameters. For all parameters, agreement was very
low. This suggests that an array approach has a better
chance of successfully locating an electrode pair based on
maximal output as compared to the standard anatomical
approach.
The electrode array approach could be considered for
cases where the clinician has difficulty locating anatom-
ical landmarks, such as for people with thick adipose tis-
sue. The array could also be beneficial for test-retest
situations where consistent electrode placement over
time is difficult to reproduce. Variations in the EMG due
to electrode positioning can be a confounding factor in
analysis. In [22], an electrode array was also used for
measurement of EMG from the lower extremities to as-
sess this variability in the context of gait analysis. While
the objective of this work is different, the results are con-
sistent in that certain electrode pairs exhibited less vari-
ability than others. In addition, appreciable differences in
robustness were observed between electrode pairs within
the array, despite the array being placed near those
adopted in clinical practice. An automatic electrode array
approach would also provide results that are independent
of a person’s experience with electrode placement; such as
in home care or isolated healthcare environments.
Conclusion
This study supports a new approach for sEMG data collec-
tion. Typically, an electrode pair is located on the muscle
and then data are collected from that site. The proposed
method involves collecting EMG data from multiple sites
on the same muscle and using signal characteristics to
choose the electrode pair at the best location (i.e., location
that most consistently provides the strongest signal).
An electrode array approach offers a more accurate
and repeatable method of locating the best site for sEMG
data collection. The results of this study demonstrated
that RMS appears to be the best parameter to provide a
quantitative measure for electrode selection. Other para-
meters, such as WL, MNF, and MDF, had also high re-
peatability but low inter-parameter agreement with RMS,
which suggests that these parameters can provide add-
itional information that could be integrated with RMS. A
multi-parameter approach is anticipated to increase the
reliability and repeatability of electrode selection.
Table 5 Agreement by parameter
Parameter Agreement
of 56 cases %
Root mean square (RMS) 8 14.29
Mean absolute value (MAV) 7 12.5
Maximum amplitude (MAX) 7 12.5
Slope sign changes (SSC) 7 12.5
Zero crossings (ZC) 7 12.5
Waveform length (WL) 9 16.07
Mean Frequency (MNF) 2 3.57
Median Frequency (MDF) 1 1.79
Signal-to-motion artifact ratio (SMR) 0 0
Maximum-to-minimum drop in
power density ratio (DPR)
3 5.36
Signal-to-noise ratio (SNR) 4 7.14
Power spectrum deformation (Ω) 3 5.36
The condition for agreement was met when the guideline-recommended
electrode pair was the same as the electrode pair that had a ranking of 1.
Kendell et al. Journal of NeuroEngineering and Rehabilitation 2012, 9:24 Page 6 of 8
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7. Appendix
The following four spectral indices, briefly described
below, were developed by Sinderby et al. and a complete
description can be found in their paper [18].
Signal-to-motion artifact ratio (SMR)
SMR assumes that motion artifact manifests itself at fre-
quencies below 20 Hz and that the uncontaminated EMG
power spectrum is fairly linear between 0 and 20 Hz. The
SMR was computed as a ratio of the sum of all power dens-
ities for frequencies below 600 Hz and the sum of all power
densities that exceed a straight line between the axis origin
and the highest mean power density value, with a frequency
above 35 Hz. The mean power density, which represents a
smoothed version of the PSD, was obtained by averaging
PSD values over 13 consecutive points; in this work, the
PSD of the EMG data (fs = 2048 Hz) was estimated via
Welch’s averaged modified periodogram, with 1024 sample
analysis windows, overlapping by 50%, so 13 consecutive
points represents a span of 26 Hz (13 Hz above and below
the frequency of interest).
Maximum-to-minimum drop in power density
ratio (DPR)
DPR is the ratio of the highest mean power density value
and lowest mean power density value, with a frequency be-
tween 35 and 600 Hz. The mean power density was
obtained by averaging PSD values over 13 consecutive
points.
Signal-to-noise ratio (SNR)
SNR is a ratio of the signal power and noise power. The
signal power was estimated as the sum of all power dens-
ities for all frequencies below 1000 Hz. The noise power
was estimated by determining the average power density
between 500 and 1000 Hz and multiplying it by the entire
frequency range (i.e. 1000 Hz). Note that this parameter
was noted to produce falsely high values for noise in the
low frequency range (e.g. motion artifact) [18].
Power spectrum deformation (Ω)
The Ω ratio is sensitive to changes in spectral symmetry
and provides a indication of spectral deformation. It is
computed as:
Mn is the nth
spectral moment defined as:
where Pi is the power spectral density value at frequency fi.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
CK was involved in study design, data collection, data processing/analysis,
and in drafting the manuscript. EL conceptualized the study, developed the
protocol, was involved in data processing and analysis, and assisted in
drafting the manuscript. YL contributed to the study design, data collection,
and analysis. AW contributed to the study design and data collection. AC
was involved in signal processing and data analysis as well as writing the
manuscript. BH contributed to study conceptualization, and interpretation. All
authors critically revised the manuscript for important intellectual content
and have read an approved the final draft.
Acknowledgements
The researchers would to thank The Ottawa Hospital Rehabilitation Center
and the Institute of Biomedical Engineering at the University of New
Brunswick for supporting this project. Brandon Fictorie is acknowledged for
his assistance with data collection. Glen Hughes, Shawn Millar, and Christie
O'Connell are also acknowledged for technical and project support. This
project was partially funded by the Atlantic Innovation Fund.
Author details
1
The Ottawa Hospital Rehabilitation Centre, 505 Smyth Road, Ottawa, ON
K1H8M2, Canada. 2
Institute of Biomedical Engineering, UNB, 25 Dineen Drive,
Fredericton, NBE3B5A3, Canada. 3
Department of Systems and Computer
Engineering, Carleton University, 1125 Colonel By Drive, Ottawa, OntarioK1S
5B6, Canada.
Received: 27 June 2011 Accepted: 20 April 2012
Published: 26 April 2012
References
1. Beck TW, Housh TJ, Mielke M, Cramer JT, Weir JP, Malek MH, Johnson
GO: The influence of electrode placement over the innervation zone on
electromyographic amplitude and mean power frequency versus
isokinetic torque relationships. J Neurosci Meth 2007, 162:72–83.
2. Beck TW, Housh TJ, Cramer JT, Stout JR, Ryan ED, Herda TJ, Costa PB,
Defreitas JM: Electrode placement over the innervation zone affects the
low-, not the high-frequency portion of the EMG frequency spectrum.
J Electromyogr Kinesiol 2009, 19(4):660–666.
3. Beck TW, Housh TJ, Cramer JT, Weir JP: The effects of electrode placement
and innervation zone location on the electromyographic amplitude and
mean power frequency versus isometric torque relationships for the
vastus lateralis muscle. J Electromyogr Kinesiol 2008, 18(2):317–328.
4. Delagi EF, Perotto A: Anatomic Guide for the Electromyographer. 2nd ed.
Springfield, IL: Charles C Thomas; 1980.
5. SENIAM 5: The State of the Art on Sensors and Sensor Placement Procedures
for Surface ElectroMyoGraphy: A proposal for sensor placement procedures.
Edited by Hermens HJ, Freriks B. Roessingh Research and Development:
Enschede, the Netherlands; 1997.
6. Farina D, Madeline P, Graven-Nielsen T, Merletti R, Arendt-Nielsen L:
Standardising surface electromyogram recordings for assessment of
activity and fatigue in the human upper trapezius muscle. Eur J Appl
Physiol 2002, 86(6):469–478.
7. Nishihara K, Kawai H, Gomi T, Terajima M, Chiba Y: Investigation of
optimum electrode locations by using automated surface
electromyography analysis technique. IEEE Trans Biomed Eng 2008, 55
(2):636–642.
8. Masuda T, Miyano H, Sadoyama T: The position of innervation zones in
the biceps brachii investigated by surface electromyography. IEEE Trans
Biomed Eng 1985, 32(1):36–42.
9. Zipp P: Recommendations for the placement of lead positions in surface
electromyography. Eur J Appl Physiol 1982, 50:41–54.
10. Day S: Important Factors in Surface EMG Measurement. Calgary: Bortech
Biomedical Ltd.; 2002.
11. Rainoldi A, Melchiorri G, Caruso I: A method for positioning electrodes
during surface EMG recordings in lower limb muscles. J Neurosci Methods
2004, 134(1):37–43.
12. Farina D, Merletti R, Stegeman DF: Biophysics of the generation of EMG
signals. In Electromyography physiology, engineering, and noninvasive
Ω ¼
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
M2=M0
p
M1=M0
Mn ¼ ∑
imax
i¼0
Pif n
i
Kendell et al. Journal of NeuroEngineering and Rehabilitation 2012, 9:24 Page 7 of 8
http://www.jneuroengrehab.com/content/9/1/24
8. applications. Edited by Merletti R, Parker PA. Hoboken: John Wiley & Sons;
2004:85–105.
13. Merletti R, Farina D, Granata A: Non-invasive assessment of motor unit
properties with linear electrode arrays. Electroencephalogr Clin Neurophysiol
1999, 50(Suppl):293–300.
14. Merletti R, Farina D, Gazzoni M: The linear electrode array: a useful tool
with many applications. J Electromyogr Kinesiol 2003, 13(1):37–47.
15. Englehart K, Hudgins B, Chan ADC: Continuous multifunction myoelectric
control using pattern recognition. Technol Disabil 2003, 15(2):95–103.
16. Öberg T, Sandsjö L, Kadefors R: EMG mean power frequency: Obtaining a
reference value. Clin Biomech 1994, 9(4):253–257.
17. Winter, DA: Biomechanics and Motor Control of Human Movement. 3rd
edition. Hoboken, NJ: John Wiley and Sons Inc.; 2005.
18. Sinderby C, Lindstrom L, Grassino AE: Automatic assessment of
electromyogram quality. J Appl Physiol 1995, 79(5):1803–1815.
19. Mesin L, Merletti R, Vieira TM: Insights gained into the interpretation of
surface electromyograms from the gastrocnemius muscles: A simulation
study. J Biomech 2011, 44(6):1096–1103.
20. Vieira TM, Loram ID, Muceli S, Merletti R, Farina D: Postural activation of the
human medial gastrocnemius muscle: are the muscle units spatially
localised?. J Physiol 2011, 589:431–443.
21. Mesin L, Farina D: Simulation of surface EMG signals generated by muscle
tissues with inhomogeneity due to fiber pinnation. IEEE Trans Biomed Eng
2004, 9:1521–1529.
22. Campanini I, Merlo A, Degola P, Merletti R, Vezzosi G, Farina D: Effect of
electrode location on EMG signal envelope in leg muscles during gait.
J Electromyogr Kinesiol 2007, 17(4):515–526.
doi:10.1186/1743-0003-9-24
Cite this article as: Kendell et al.: A novel approach to surface
electromyography: an exploratory study of electrode-pair selection based
on signal characteristics. Journal of NeuroEngineering and Rehabilitation 2012
9:24.
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