Electromyogram (EMG) is the record of the electrical excitation of the skeletal
muscles which is initiated and regulated by the central and peripheral nervous system.
EMGs have non-stationary properties. EMG signals of isometric contraction for two
different abnormalities namely ALS (Amyotrophic Lateral Sclerosis) which is coming under
Neuropathy and Myopathy. Neuropathy relates to the degeneration of neural impulse
whereas myopathy relates to the degeneration of muscle fibers. There are two issues in the
classification of EMG signals. In EMG’s diseases recognition, the first and the most
important step is feature extraction. In this paper, six non-linear features have been used to
classify using Support Vector Machine. In this paper, after feature extraction, feature
matrix is normalized in order to have features in a same range. Simply, linear SVM
classifier was trained by the train-train data and then used for classifying the train-test
data. From the experimental results, Lyapunov exponent and Hurst exponent is the best
feature with higher accuracy comparing with the other features, whereas features like
Capacity Dimension, Correlation Function, Correlation Dimension, Probability Distribution
& Correlation Matrix are useful augmenting features.
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.
Bio-medical (EMG) Signal Analysis and Feature Extraction Using Wavelet TransformIJERA Editor
This document summarizes research on analyzing electromyography (EMG) signals using wavelet transforms to extract features for classification of muscle activity. A multi-channel EMG acquisition system was developed using surface electrodes to measure forearm muscle signals. Different wavelet families were used to analyze the EMG signals. Features like root mean square, logarithm of root mean square, centroid frequency, and standard deviation were extracted. Root mean square feature extraction performed best. In the future, this method could be used to control prosthetic or robotic arms for real-time processing based on muscle activity.
Reconstruction of electrical impedance tomography images based on the expecta...ISA Interchange
Electrical impedance tomography (EIT) calculates the internal conductivity distribution within a body using electrical contact measurements. The image reconstruction for EIT is an inverse problem, which is both non-linear and ill-posed. The traditional regularization method cannot avoid introducing negative values in the solution. The negativity of the solution produces artifacts in reconstructed images in presence of noise. A statistical method, namely, the expectation maximization (EM) method, is used to solve the inverse problem for EIT in this paper. The mathematical model of EIT is transformed to the non-negatively constrained likelihood minimization problem. The solution is obtained by the gradient projection-reduced Newton (GPRN) iteration method. This paper also discusses the strategies of choosing parameters. Simulation and experimental results indicate that the reconstructed images with higher quality can be obtained by the EM method, compared with the traditional Tikhonov and conjugate gradient (CG) methods, even with non-negative processing.
This document discusses estimating hand muscle power using surface electromyography (EMG). EMG is used to evaluate electrical activity in muscles during activities to grade muscle strength. The research aims to develop an automatic method for grading muscle power. EMG is acquired from hand muscles during different activities and analyzed. Analysis includes root mean square, maximum amplitude, and burst time of EMG signals. Results from fifty young subjects show these metrics increase with greater muscle contraction and resistance, allowing muscle strength grading. The method could improve on manual muscle testing which depends on examiner judgment.
A Novel Approach to Study the Effects of Anesthesia on Respiratory Signals by...IJECEIAES
General anesthesia plays a crucial role in many surgical procedures, and it therefore has an enormous impact on human health. There are no precise measures for maintaining the correct dose of anesthetic, and there is currently no fully reliable instrument to monitor depth of anesthesia. In this paper, a novel approach has been proposed for detecting the changes in synchronism of brain signals, taken from EEG machine. During the effect of anesthesia, there are certain changes in the EEG signals. Those signals show changes in their synchronism. This phenomenon of synchronism can be utilized to study the effect of anesthesia on respiratory parameters like respiration rate etc, and hence the quantity of anesthesia can be regulated, and if any problem occurs in breathing during the effect of anesthesia on patient, that can also be monitored.
This study investigates the concordance between resting-state functional MRI (rs-fMRI) and stereotactically-implanted EEG (sEEG) in measuring interactions between brain regions in epilepsy patients. Electrodes were implanted in 13 brain regions and rs-fMRI data was collected. Coherence between sEEG electrode pairs and correlation between corresponding rs-fMRI regions were calculated. Strong similarities were found between the sEEG coherence and rs-fMRI correlation matrices, though some regions differed. Certain region pairs, like between the precuneus and posterior cingulate cortex, showed particularly high interaction levels in both modalities. This preliminary analysis indicates potential for rs-fMRI to identify connectivity patterns seen in sEEG.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
General anesthesia plays a crucial role in many surgical procedures. It is a drug-induced, reversible state characterized by unconsciousness, anti-nociception or analgesia, immobility and amnesia. On rare occasions, however, the patient can remain unconscious longer than intended, or may regain awareness during surgery. There are no precise measures for maintaining the correct dose of anesthetic, and there is currently no fully reliable instrument to monitor depth of anesthesia. Although a number of devices for monitoring brain function or sympathetic output are commercially available, the anesthetist also relies on clinical assessment and experience to judge anesthetic depth. The undesirable consequences of overdose or unintended awareness might in principle be ameliorated by improved control if we could understand better the changes in function that occur during general anesthesia. Coupling functions prescribe the physical rule specifying how the inter-oscillator interactions occur. They determine the possibility of qualitative transitions between the oscillations, e.g. routes into and out of phase synchronization. Their decomposition can describe the functional contribution from each separate subsystem within a single coupling relationship. In this way, coupling functions offer a unique means of describing mechanisms in a unified and mathematically precise way. It is a fast growing field of research, with much recent progress on the theory and especially towards being able to extract and reconstruct the coupling functions between interacting oscillations from data, leading to useful applications in cardio respiratory interactions. In this paper, a novel approach has been proposed for detecting the changes in synchronism of brain signals, taken from EEG machine. During the effect of anesthesia, there are certain changes in the EEG signals. Those signals show changes in their synchronism. This phenomenon of synchronism can be utilized to study the effect of anesthesia on respiratory parameters like respiration rate etc, and hence the quantity of anesthesia can be regulated, and if any problem occurs in breathing during the effect of anesthesia on patient, that can also be monitored.
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.
Bio-medical (EMG) Signal Analysis and Feature Extraction Using Wavelet TransformIJERA Editor
This document summarizes research on analyzing electromyography (EMG) signals using wavelet transforms to extract features for classification of muscle activity. A multi-channel EMG acquisition system was developed using surface electrodes to measure forearm muscle signals. Different wavelet families were used to analyze the EMG signals. Features like root mean square, logarithm of root mean square, centroid frequency, and standard deviation were extracted. Root mean square feature extraction performed best. In the future, this method could be used to control prosthetic or robotic arms for real-time processing based on muscle activity.
Reconstruction of electrical impedance tomography images based on the expecta...ISA Interchange
Electrical impedance tomography (EIT) calculates the internal conductivity distribution within a body using electrical contact measurements. The image reconstruction for EIT is an inverse problem, which is both non-linear and ill-posed. The traditional regularization method cannot avoid introducing negative values in the solution. The negativity of the solution produces artifacts in reconstructed images in presence of noise. A statistical method, namely, the expectation maximization (EM) method, is used to solve the inverse problem for EIT in this paper. The mathematical model of EIT is transformed to the non-negatively constrained likelihood minimization problem. The solution is obtained by the gradient projection-reduced Newton (GPRN) iteration method. This paper also discusses the strategies of choosing parameters. Simulation and experimental results indicate that the reconstructed images with higher quality can be obtained by the EM method, compared with the traditional Tikhonov and conjugate gradient (CG) methods, even with non-negative processing.
This document discusses estimating hand muscle power using surface electromyography (EMG). EMG is used to evaluate electrical activity in muscles during activities to grade muscle strength. The research aims to develop an automatic method for grading muscle power. EMG is acquired from hand muscles during different activities and analyzed. Analysis includes root mean square, maximum amplitude, and burst time of EMG signals. Results from fifty young subjects show these metrics increase with greater muscle contraction and resistance, allowing muscle strength grading. The method could improve on manual muscle testing which depends on examiner judgment.
A Novel Approach to Study the Effects of Anesthesia on Respiratory Signals by...IJECEIAES
General anesthesia plays a crucial role in many surgical procedures, and it therefore has an enormous impact on human health. There are no precise measures for maintaining the correct dose of anesthetic, and there is currently no fully reliable instrument to monitor depth of anesthesia. In this paper, a novel approach has been proposed for detecting the changes in synchronism of brain signals, taken from EEG machine. During the effect of anesthesia, there are certain changes in the EEG signals. Those signals show changes in their synchronism. This phenomenon of synchronism can be utilized to study the effect of anesthesia on respiratory parameters like respiration rate etc, and hence the quantity of anesthesia can be regulated, and if any problem occurs in breathing during the effect of anesthesia on patient, that can also be monitored.
This study investigates the concordance between resting-state functional MRI (rs-fMRI) and stereotactically-implanted EEG (sEEG) in measuring interactions between brain regions in epilepsy patients. Electrodes were implanted in 13 brain regions and rs-fMRI data was collected. Coherence between sEEG electrode pairs and correlation between corresponding rs-fMRI regions were calculated. Strong similarities were found between the sEEG coherence and rs-fMRI correlation matrices, though some regions differed. Certain region pairs, like between the precuneus and posterior cingulate cortex, showed particularly high interaction levels in both modalities. This preliminary analysis indicates potential for rs-fMRI to identify connectivity patterns seen in sEEG.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
General anesthesia plays a crucial role in many surgical procedures. It is a drug-induced, reversible state characterized by unconsciousness, anti-nociception or analgesia, immobility and amnesia. On rare occasions, however, the patient can remain unconscious longer than intended, or may regain awareness during surgery. There are no precise measures for maintaining the correct dose of anesthetic, and there is currently no fully reliable instrument to monitor depth of anesthesia. Although a number of devices for monitoring brain function or sympathetic output are commercially available, the anesthetist also relies on clinical assessment and experience to judge anesthetic depth. The undesirable consequences of overdose or unintended awareness might in principle be ameliorated by improved control if we could understand better the changes in function that occur during general anesthesia. Coupling functions prescribe the physical rule specifying how the inter-oscillator interactions occur. They determine the possibility of qualitative transitions between the oscillations, e.g. routes into and out of phase synchronization. Their decomposition can describe the functional contribution from each separate subsystem within a single coupling relationship. In this way, coupling functions offer a unique means of describing mechanisms in a unified and mathematically precise way. It is a fast growing field of research, with much recent progress on the theory and especially towards being able to extract and reconstruct the coupling functions between interacting oscillations from data, leading to useful applications in cardio respiratory interactions. In this paper, a novel approach has been proposed for detecting the changes in synchronism of brain signals, taken from EEG machine. During the effect of anesthesia, there are certain changes in the EEG signals. Those signals show changes in their synchronism. This phenomenon of synchronism can be utilized to study the effect of anesthesia on respiratory parameters like respiration rate etc, and hence the quantity of anesthesia can be regulated, and if any problem occurs in breathing during the effect of anesthesia on patient, that can also be monitored.
A comparative study of wavelet families for electromyography signal classific...journalBEEI
The document presents a study comparing different wavelet families for classifying electromyography (EMG) signals based on discrete wavelet transform (DWT). The proposed method involves decomposing EMG signals into sub-bands using DWT, extracting statistical features from each sub-band, and using support vector machines (SVM) for classification. Results showed that the sym14 wavelet at the 8th decomposition level achieved the best classification performance for detecting neuromuscular disorders. The study demonstrates that the proposed DWT-based approach can effectively classify EMG signals and help diagnose neuromuscular conditions.
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.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
This paper will review the works on Surface Electromyography (SEMG) signal acquisition and controlling as well as the uses of SEMG signals analysis for Transfemoral amputee's people. In the beginning, this paper will briefly go through the basic theory of myoelectric signal generation. Next, the signal acquisition & filtering techniques applied for SEMG signal will be explained. Then after this EMG signal control or actuate the myoelectric leg who was suffering from Transfemoral amputee using microcontroller. This paper gives the better controlling SEMG signal and also very smooth and easy controlling of the Prosthetic leg motor using Myoelectric Controller.
Detection of epileptic indicators on clinical subbands of eegOlusola Adeyemi
This document summarizes a research paper that aims to detect epileptic indicators in EEG signals using nonlinear analysis techniques. It discusses prior work using linear and nonlinear methods for EEG analysis. The proposed method models chaotic measures on standard clinical subbands of EEG signals, focusing on the gamma subband. It describes constructing time delay vectors from the gamma subband signals using determined optimum lag and minimum embedding dimension. Recurrence rate is used to model the chaotic behavior and derive feature vectors representing discriminating characteristics between healthy, interictal, and ictal EEG signals. Classification performance using these features is evaluated.
IRJET- Analysis of Epilepsy using Approximate Entropy AlgorithmIRJET Journal
The document analyzes epilepsy using the Approximate Entropy (ApEn) algorithm. ApEn is used to measure irregularity and unpredictability in EEG signals. The study applies the ApEn algorithm to EEG data from 4 subjects - 2 non-epileptic and 2 epileptic. For each subject, EEG data was recorded under different conditions, such as eyes open/closed, during seizures, etc. Integrated ApEn waveforms were generated for 4 selected data sets to analyze and distinguish epileptic from non-epileptic EEG signals. The results showed that the integrated ApEn waveform had minimal variation for the non-epileptic subject with eyes closed, and slightly more variation for the non-epileptic subject with eyes
This study aimed to non-invasively detect electromyography (EMG) activity of deep thumb muscles using surface electrodes. Researchers placed electrodes on the forearms of 15 participants and recorded EMG signals while participants performed thumb movements. Independent component analysis was used to separate EMG signals from superficial and deep muscles. Predicted EMG waveforms for each deep muscle were correlated with independent components, and the highest correlated component was considered to represent that muscle's activity. Overall high correlations were found between predicted and recorded waveforms. Accuracy, sensitivity and specificity measures between predicted and recorded waveforms were also statistically significant when using a threshold activation level, demonstrating the first non-invasive detection of EMG activity from deep thumb muscles.
ECG Signal Analysis for Myocardial Infarction DetectionUzair Akbar
Myocardial Infarction is one of the fatal heart diseases. It is essential that a patient is monitored for the early detection of MI. Owing to the newer technology such as wearable sensors which are capable of transmitting wirelessly, this can be done easily. However, there is a need for real-time applications that are able to accurately detect MI non-invasively. This project studies a prospective method by which we can detect MI. Our approach analyses the ECG (electrocardiogram) of a patient in real-time and extracts the ST elevation from each cycle. The ST elevation plays an important part in MI detection. We then use the sequential change point detection algorithm; CUmulative SUM (CUSUM), to detect any deviation in the ST elevation spectrum and to raise an alarm if we find any.
Myocardial Infarction is one of the fatal heart diseases. It is essential that a patient is monitored for the early detection of MI. Owing to the newer technology such as wearable sensors which are capable of transmitting wirelessly, this can be done easily. However, there is a need for real-time applications that are able to accurately detect MI non-invasively. This project studies a prospective method by which we can detect MI. Our approach analyses the ECG (electrocardiogram) of a patient in real-time and extracts the ST elevation from each cycle. The ST elevation plays an important part in MI detection. We then use the sequential change point detection algorithm; CUmulative SUM (CUSUM), to detect any deviation in the ST elevation spectrum and to raise an alarm if we find any.
An Artificial Neural Network Model for Classification of Epileptic Seizures U...ijsc
Epilepsy is one of the most common neurological disorders characterized by transient and unexpected
electrical disturbance in the brain. In This paper the EEG signals are decomposed into a finite set of band
limited signals termed as Intrinsic mode functions. The Hilbert transom is applied on these IMF’s to
calculate instantaneous frequencies. The 2nd,3rd and 4th IMF's are used to extract features of epileptic
signal. A neural network using back propagation algorithm is implemented for classification of epilepsy.
An overall accuracy of 99.8% is achieved in classification..
An artificial neural network model for classification of epileptic seizures u...ijsc
Epilepsy is one of the most common neurological disorders characterized by transient and unexpected
electrical disturbance in the brain. In This paper
the EEG signals are decomposed into a finite set of
bandlimited signals termed as Intrinsic mode functions.
The Hilbert transom is applied on these IMF’s to
calculate instantaneous frequencies. The 2nd,3rd an
d 4th IMF's are used to extract features of epilepticsignal. A neural network using back propagation alg
orithm is implemented for classification of epilepsy.An overall accuracy of 99.8% is achieved in classification..
In many situations, the Electrocardiogram (ECG) is
recorded during ambulatory or strenuous conditions such that the
signal is corrupted by different types of noise, sometimes
originating from another physiological process of the body. Hence,
noise removal is an important aspect of signal processing. Here five
different filters i.e. median, Low Pass Butter worth, FIR, Weighted
Moving Average and Stationary Wavelet Transform (SWT) with
their filtering effect on noisy ECG are presented. Comparative
analyses among these filtering techniques are described and
statically results are evaluated.
Wavelet-based EEG processing for computer-aided seizure detection and epileps...IJERA Editor
Many Neurological disorders are very difficult to detect. One such Neurological disorder which we are going to discuss in this paper is Epilepsy. Epilepsy means sudden change in the behavior of a human being for a short period of time. This is caused due to seizures in the brain. Many researches are going onto detect epilepsy detection through analyzing EEG. One such method of epilepsy detection is proposed in this paper. This technique employs Discrete Wave Transform (DWT) method for pre-processing, Approximate Entropy (ApEn) to extract features and Artificial Neural Network (ANN) for classification. This paper presented a detailed survey of various methods that are being used for epilepsy detection and also proposes a wavelet based epilepsy detection method
EEG based Brain Computer Interface (BCI) establishes a new channel between human brain and the
surrounding environment in order to disseminate instructions to the outside world. It is based on the
recording of temporary EEG changes during different types of motor imagery such as imagination of
different hand movements. The spatial pattern of activated cortical areas during motor imagery is similar
to that of real time executed movement. Time domain features and frequency domain features are extracted
with emphasis on recognizing discriminative features representing EEG trials recorded during imagination
of different hand movements. Then, classification into different hand movements is carried out.
This document summarizes a study that aimed to replicate the results of a previous paper on selectively stimulating neuronal fibers or cell bodies using different asymmetric biphasic current waveforms. The study developed a multi-compartment Hodgkin-Huxley neuronal model in MATLAB and simulated the response of populations of neurons to different stimulus waveforms. The results showed that an anodic-leading asymmetric biphasic waveform selectively activated fibers, while a cathodic-leading waveform preferentially activated cell bodies, consistent with the previous study.
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.
Classification of EEG Signal for Epileptic Seizure DetectionusingEMD and ELMIJTET Journal
Abstract—This paper proposes the classification of EEG signal for epilepsy diagnosis. Epilepsy is a neurological disorder which occurs due to synchronous neuronal activity in brain. Empirical Mode Decomposition (EMD), Extreme Learning Machine (ELM) are the techniquedelivered in the proposed method.Input EEG signal, which is available in online as Bonn Database is decomposed into five Intrinsic Mode Functions (IMFs) using EMD.Higher Order Statistical moments such as Variance, Skewness and Kurtosis are drawn out as features from the decomposed signals. Extreme Learning Machine is used as a classifier to classify the EEG signals with the taken features, under various categories that include healthy and ictal, interictal and ictal, Non seizure and seizure, healthy, interictal and ictal. The proposed method gives 100%accuracy, 100%sensitivity in discriminating interictal and ictal, non seizure and seizure, healthy and ictal, healthy, interictal and ictal, 100% specificity in classifying healthy and ictal, interictal and ictal and 100% and 99%accuracy in case of discriminating interictal and ictal, non seizure and seizure.
A New Approach to Denoising EEG Signals - Merger of Translation Invariant Wav...CSCJournals
In this paper we present a new algorithm using a merger of Independent Component Analysis and Translation Invariant Wavelet Transform. The efficacy of this algorithm is evaluated by applying contaminated EEG signals. Its performance was compared to three fixed-point ICA algorithms (FastICA, EFICA and Pearson-ICA) using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Signal to Distortion Ratio (SDR), and Amari Performance Index. Experiments reveal that our new technique is the most accurate separation method.
The document discusses ECG signal analysis and abnormality detection using artificial neural networks. It defines normal and abnormal ECG signals, describing abnormalities like bradycardia and tachycardia. Two algorithms are described for detecting abnormalities: one analyzes heart rate and the other detects general heart diseases. An ANN system is used for ECG analysis and classification, taking spectral entropy, Poincare plot geometry, and largest Lyapunov exponent as inputs to classify eight cardiac conditions.
This document summarizes a research paper that proposes using an artificial neural network tuned by a simulated annealing algorithm for real-time credit card fraud detection. The paper describes how simulated annealing can be used to train the weights of a neural network model to classify credit card transactions as fraudulent or non-fraudulent based on attributes of past transactions. The algorithm is tested on a real-world credit card transaction dataset and is found to effectively classify most transactions correctly, though some misclassifications still occur.
Wireless sensor networks (WSN) have been widely used in various applications.
In these networks nodes collect data from the attached sensors and send their data to a base
station. However, nodes in WSN have limited power supply in form of battery so the nodes
are expected to minimize energy consumption in order to maximize the lifetime of WSN. A
number of techniques have been proposed in the literature to reduce the energy
consumption significantly. In this paper, we propose a new clustering based technique
which is a modification of the popular LEACH algorithm. In this technique, first cluster
heads are elected using the improved LEACH algorithm as usual, and then a cluster of
nodes is formed based on the distance between node and cluster head. Finally, data from
node is transferred to cluster head. Cluster heads forward data, after applying aggregation,
to the cluster head that is closer to it than sink in forward direction or directly to the sink.
This reduction in distance travelled improves the performance over LEACH algorithm
significantly.
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A comparative study of wavelet families for electromyography signal classific...journalBEEI
The document presents a study comparing different wavelet families for classifying electromyography (EMG) signals based on discrete wavelet transform (DWT). The proposed method involves decomposing EMG signals into sub-bands using DWT, extracting statistical features from each sub-band, and using support vector machines (SVM) for classification. Results showed that the sym14 wavelet at the 8th decomposition level achieved the best classification performance for detecting neuromuscular disorders. The study demonstrates that the proposed DWT-based approach can effectively classify EMG signals and help diagnose neuromuscular conditions.
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.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
This paper will review the works on Surface Electromyography (SEMG) signal acquisition and controlling as well as the uses of SEMG signals analysis for Transfemoral amputee's people. In the beginning, this paper will briefly go through the basic theory of myoelectric signal generation. Next, the signal acquisition & filtering techniques applied for SEMG signal will be explained. Then after this EMG signal control or actuate the myoelectric leg who was suffering from Transfemoral amputee using microcontroller. This paper gives the better controlling SEMG signal and also very smooth and easy controlling of the Prosthetic leg motor using Myoelectric Controller.
Detection of epileptic indicators on clinical subbands of eegOlusola Adeyemi
This document summarizes a research paper that aims to detect epileptic indicators in EEG signals using nonlinear analysis techniques. It discusses prior work using linear and nonlinear methods for EEG analysis. The proposed method models chaotic measures on standard clinical subbands of EEG signals, focusing on the gamma subband. It describes constructing time delay vectors from the gamma subband signals using determined optimum lag and minimum embedding dimension. Recurrence rate is used to model the chaotic behavior and derive feature vectors representing discriminating characteristics between healthy, interictal, and ictal EEG signals. Classification performance using these features is evaluated.
IRJET- Analysis of Epilepsy using Approximate Entropy AlgorithmIRJET Journal
The document analyzes epilepsy using the Approximate Entropy (ApEn) algorithm. ApEn is used to measure irregularity and unpredictability in EEG signals. The study applies the ApEn algorithm to EEG data from 4 subjects - 2 non-epileptic and 2 epileptic. For each subject, EEG data was recorded under different conditions, such as eyes open/closed, during seizures, etc. Integrated ApEn waveforms were generated for 4 selected data sets to analyze and distinguish epileptic from non-epileptic EEG signals. The results showed that the integrated ApEn waveform had minimal variation for the non-epileptic subject with eyes closed, and slightly more variation for the non-epileptic subject with eyes
This study aimed to non-invasively detect electromyography (EMG) activity of deep thumb muscles using surface electrodes. Researchers placed electrodes on the forearms of 15 participants and recorded EMG signals while participants performed thumb movements. Independent component analysis was used to separate EMG signals from superficial and deep muscles. Predicted EMG waveforms for each deep muscle were correlated with independent components, and the highest correlated component was considered to represent that muscle's activity. Overall high correlations were found between predicted and recorded waveforms. Accuracy, sensitivity and specificity measures between predicted and recorded waveforms were also statistically significant when using a threshold activation level, demonstrating the first non-invasive detection of EMG activity from deep thumb muscles.
ECG Signal Analysis for Myocardial Infarction DetectionUzair Akbar
Myocardial Infarction is one of the fatal heart diseases. It is essential that a patient is monitored for the early detection of MI. Owing to the newer technology such as wearable sensors which are capable of transmitting wirelessly, this can be done easily. However, there is a need for real-time applications that are able to accurately detect MI non-invasively. This project studies a prospective method by which we can detect MI. Our approach analyses the ECG (electrocardiogram) of a patient in real-time and extracts the ST elevation from each cycle. The ST elevation plays an important part in MI detection. We then use the sequential change point detection algorithm; CUmulative SUM (CUSUM), to detect any deviation in the ST elevation spectrum and to raise an alarm if we find any.
Myocardial Infarction is one of the fatal heart diseases. It is essential that a patient is monitored for the early detection of MI. Owing to the newer technology such as wearable sensors which are capable of transmitting wirelessly, this can be done easily. However, there is a need for real-time applications that are able to accurately detect MI non-invasively. This project studies a prospective method by which we can detect MI. Our approach analyses the ECG (electrocardiogram) of a patient in real-time and extracts the ST elevation from each cycle. The ST elevation plays an important part in MI detection. We then use the sequential change point detection algorithm; CUmulative SUM (CUSUM), to detect any deviation in the ST elevation spectrum and to raise an alarm if we find any.
An Artificial Neural Network Model for Classification of Epileptic Seizures U...ijsc
Epilepsy is one of the most common neurological disorders characterized by transient and unexpected
electrical disturbance in the brain. In This paper the EEG signals are decomposed into a finite set of band
limited signals termed as Intrinsic mode functions. The Hilbert transom is applied on these IMF’s to
calculate instantaneous frequencies. The 2nd,3rd and 4th IMF's are used to extract features of epileptic
signal. A neural network using back propagation algorithm is implemented for classification of epilepsy.
An overall accuracy of 99.8% is achieved in classification..
An artificial neural network model for classification of epileptic seizures u...ijsc
Epilepsy is one of the most common neurological disorders characterized by transient and unexpected
electrical disturbance in the brain. In This paper
the EEG signals are decomposed into a finite set of
bandlimited signals termed as Intrinsic mode functions.
The Hilbert transom is applied on these IMF’s to
calculate instantaneous frequencies. The 2nd,3rd an
d 4th IMF's are used to extract features of epilepticsignal. A neural network using back propagation alg
orithm is implemented for classification of epilepsy.An overall accuracy of 99.8% is achieved in classification..
In many situations, the Electrocardiogram (ECG) is
recorded during ambulatory or strenuous conditions such that the
signal is corrupted by different types of noise, sometimes
originating from another physiological process of the body. Hence,
noise removal is an important aspect of signal processing. Here five
different filters i.e. median, Low Pass Butter worth, FIR, Weighted
Moving Average and Stationary Wavelet Transform (SWT) with
their filtering effect on noisy ECG are presented. Comparative
analyses among these filtering techniques are described and
statically results are evaluated.
Wavelet-based EEG processing for computer-aided seizure detection and epileps...IJERA Editor
Many Neurological disorders are very difficult to detect. One such Neurological disorder which we are going to discuss in this paper is Epilepsy. Epilepsy means sudden change in the behavior of a human being for a short period of time. This is caused due to seizures in the brain. Many researches are going onto detect epilepsy detection through analyzing EEG. One such method of epilepsy detection is proposed in this paper. This technique employs Discrete Wave Transform (DWT) method for pre-processing, Approximate Entropy (ApEn) to extract features and Artificial Neural Network (ANN) for classification. This paper presented a detailed survey of various methods that are being used for epilepsy detection and also proposes a wavelet based epilepsy detection method
EEG based Brain Computer Interface (BCI) establishes a new channel between human brain and the
surrounding environment in order to disseminate instructions to the outside world. It is based on the
recording of temporary EEG changes during different types of motor imagery such as imagination of
different hand movements. The spatial pattern of activated cortical areas during motor imagery is similar
to that of real time executed movement. Time domain features and frequency domain features are extracted
with emphasis on recognizing discriminative features representing EEG trials recorded during imagination
of different hand movements. Then, classification into different hand movements is carried out.
This document summarizes a study that aimed to replicate the results of a previous paper on selectively stimulating neuronal fibers or cell bodies using different asymmetric biphasic current waveforms. The study developed a multi-compartment Hodgkin-Huxley neuronal model in MATLAB and simulated the response of populations of neurons to different stimulus waveforms. The results showed that an anodic-leading asymmetric biphasic waveform selectively activated fibers, while a cathodic-leading waveform preferentially activated cell bodies, consistent with the previous study.
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.
Classification of EEG Signal for Epileptic Seizure DetectionusingEMD and ELMIJTET Journal
Abstract—This paper proposes the classification of EEG signal for epilepsy diagnosis. Epilepsy is a neurological disorder which occurs due to synchronous neuronal activity in brain. Empirical Mode Decomposition (EMD), Extreme Learning Machine (ELM) are the techniquedelivered in the proposed method.Input EEG signal, which is available in online as Bonn Database is decomposed into five Intrinsic Mode Functions (IMFs) using EMD.Higher Order Statistical moments such as Variance, Skewness and Kurtosis are drawn out as features from the decomposed signals. Extreme Learning Machine is used as a classifier to classify the EEG signals with the taken features, under various categories that include healthy and ictal, interictal and ictal, Non seizure and seizure, healthy, interictal and ictal. The proposed method gives 100%accuracy, 100%sensitivity in discriminating interictal and ictal, non seizure and seizure, healthy and ictal, healthy, interictal and ictal, 100% specificity in classifying healthy and ictal, interictal and ictal and 100% and 99%accuracy in case of discriminating interictal and ictal, non seizure and seizure.
A New Approach to Denoising EEG Signals - Merger of Translation Invariant Wav...CSCJournals
In this paper we present a new algorithm using a merger of Independent Component Analysis and Translation Invariant Wavelet Transform. The efficacy of this algorithm is evaluated by applying contaminated EEG signals. Its performance was compared to three fixed-point ICA algorithms (FastICA, EFICA and Pearson-ICA) using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Signal to Distortion Ratio (SDR), and Amari Performance Index. Experiments reveal that our new technique is the most accurate separation method.
The document discusses ECG signal analysis and abnormality detection using artificial neural networks. It defines normal and abnormal ECG signals, describing abnormalities like bradycardia and tachycardia. Two algorithms are described for detecting abnormalities: one analyzes heart rate and the other detects general heart diseases. An ANN system is used for ECG analysis and classification, taking spectral entropy, Poincare plot geometry, and largest Lyapunov exponent as inputs to classify eight cardiac conditions.
This document summarizes a research paper that proposes using an artificial neural network tuned by a simulated annealing algorithm for real-time credit card fraud detection. The paper describes how simulated annealing can be used to train the weights of a neural network model to classify credit card transactions as fraudulent or non-fraudulent based on attributes of past transactions. The algorithm is tested on a real-world credit card transaction dataset and is found to effectively classify most transactions correctly, though some misclassifications still occur.
Wireless sensor networks (WSN) have been widely used in various applications.
In these networks nodes collect data from the attached sensors and send their data to a base
station. However, nodes in WSN have limited power supply in form of battery so the nodes
are expected to minimize energy consumption in order to maximize the lifetime of WSN. A
number of techniques have been proposed in the literature to reduce the energy
consumption significantly. In this paper, we propose a new clustering based technique
which is a modification of the popular LEACH algorithm. In this technique, first cluster
heads are elected using the improved LEACH algorithm as usual, and then a cluster of
nodes is formed based on the distance between node and cluster head. Finally, data from
node is transferred to cluster head. Cluster heads forward data, after applying aggregation,
to the cluster head that is closer to it than sink in forward direction or directly to the sink.
This reduction in distance travelled improves the performance over LEACH algorithm
significantly.
This document provides an overview of vertical handover decision strategies in heterogeneous wireless networks. It begins with an introduction to always best connectivity requirements in next generation networks that allow users to move between different network technologies. It then discusses the key aspects of handover management, including the three phases of initiation, decision, and execution. Various criteria for the handover decision process are described, such as received signal strength, network connection time, available bandwidth, power consumption, cost, security, and user preferences. Different types of handover decision strategies are categorized, including those based on network conditions, user preferences, multiple attributes, fuzzy logic/neural networks, and context awareness. The strategies are analyzed and their advantages/disadvantages compared.
This paper presents the design and performance comparison of a two stage
operational amplifier topology using CMOS and BiCMOS technology. This conventional op
amp circuit was designed by using RF model of BSIM3V3 in 0.6 μm CMOS technology and
0.35 μm BiCMOS technology. Both the op amp circuits were designed and simulated,
analyzed and performance parameters are compared. The performance parameters such as
gain, phase margin, CMRR, PSRR, power consumption etc achieved are compared. Finally,
we conclude the suitability of CMOS technology over BiCMOS technology for low power
RF design.
In Cognitive Radio Networks (CRN), Cooperative Spectrum Sensing (CSS) is
used to improve performance of spectrum sensing techniques used for detection of licensed
(Primary) user’s signal. In CSS, the spectrum sensing information from multiple unlicensed
(Secondary) users are combined to take final decision about presence of primary signal. The
mixing techniques used to generate final decision about presence of PU’s signal are also
called as Fusion techniques / rules. The fusion techniques are further classified as data
fusion and decision fusion techniques. In data fusion technique all the secondary users
(SUs) share their raw information of spectrum detection like detected energy or other
statistical information, while in decision fusion technique all the SUs take their local
decisions and share the decision by sending ‘0’ or ‘1’ corresponding to absence and presence
of PU’s signal respectively. The rules used in decision fusion techniques are OR rule, AND
rule and K-out-of-N rule. The CSS is further classified as distributed CSS and centralized
CSS. In distributed CSS all the SUs share the spectrum detection information with each
other and by mixing the shared information; all the SUs take final decision individually. In
centralized CSS all the SUs send their detected information to a secondary base station /
central unit which combines the shared information and takes final decision. The secondary
base station shares the final decision with all the SUs in the CRN. This paper covers
overview of information fusion methods used for CSS and analysis of decision fusion rules
with simulation results.
This paper analyzes the impact of network scalability on various physical attributes of Zigbee networks. Simulations were conducted using Qualnet to evaluate the performance of the Zigbee physical layer based on energy consumption and throughput. Energy consumption was analyzed for different modulation schemes (ASK, BPSK, OQPSK), network sizes (2-50 nodes), and clear channel assessment modes. The results showed that OQPSK and ASK had lower energy consumption than BPSK. Throughput was highest for OQPSK. While carrier sense had slightly higher throughput than other CCA modes, the energy consumption differences between CCA modes were minor.
This paper gives a brief idea of the moving objects tracking and its application.
In sport it is challenging to track and detect motion of players in video frames. Task
represents optical flow analysis to do motion detection and particle filter to track players
and taking consideration of regions with movement of players in sports video. Optical flow
vector calculation gives motion of players in video frame. This paper presents improved
Luacs Kanade algorithm explained for optical flow computation for large displacement and
more accuracy in motion estimation.
A rapid progress is seen in the field of robotics both in educational and industrial
automation sectors. The Robotics education in particular is gaining technological advances
and providing more learning opportunities. In automotive sector, there is a necessity and
demand to automate daily human activities by robot. With such an advancement and
demand for robotics, the realization of a popular computer game will help students to learn
and acquire skills in the field of robotics. The computer game such as Pacman offers
challenges on both software and hardware fronts. In software, it provides challenges in
developing algorithms for a robot to escape from the pool of attacking robots and to develop
algorithms for multiple ghost robots to attack the Pacman. On the hardware front, it
provides a challenge to integrate various systems to realize the game. This project aims to
demonstrate the pacman game in real world as well as in simulation. For simulation
purpose Player/Stage is used to develop single-client and multi-client architectures. The
multi- client architecture in player/stage uses one global simulation proxy to which all the
robot models are connected. This reduces the overhead to manage multiple robots proxy.
The single-client architecture enables only two robot models to connect to the simulation
proxy. Multi-client approach offers flexibility to add sensors to each port which will be used
distinctly by the client attached to the respective robot. The robots are named as Pacman
and Ghosts, which try to escape and attack respectively. Use of Network Camera has been
done to detect the global positions of the robots and data is shared through inter-process
communication.
In Content-Based Image Retrieval (CBIR) systems, the visual contents of the
images in the database are took out and represented by multi-dimensional characteristic
vectors. A well known CBIR system that retrieves images by unsupervised method known
as cluster based image retrieval system. For enhancing the performance and retrieval rate
of CBIR system, we fuse the visual contents of an image. Recently, we developed two
cluster-based CBIR systems by fusing the scores of two visual contents of an image. In this
paper, we analyzed the performance of the two recommended CBIR systems at different
levels of precision using images of varying sizes and resolutions. We also compared the
performance of the recommended systems with that of the other two existing CBIR systems
namely UFM and CLUE. Experimentally, we find that the recommended systems
outperform the other two existing systems and one recommended system also comparatively
performed better in every resolution of image.
Information Systems and Networks are subjected to electronic attacks. When
network attacks hit, organizations are thrown into crisis mode. From the IT department to
call centers, to the board room and beyond, all are fraught with danger until the situation is
under control. Traditional methods which are used to overcome these threats (e.g. firewall,
antivirus software, password protection etc.) do not provide complete security to the system.
This encourages the researchers to develop an Intrusion Detection System which is capable
of detecting and responding to such events. This review paper presents a comprehensive
study of Genetic Algorithm (GA) based Intrusion Detection System (IDS). It provides a
brief overview of rule-based IDS, elaborates the implementation issues of Genetic Algorithm
and also presents a comparative analysis of existing studies.
Step by step operations by which we make a group of objects in which attributes
of all the objects are nearly similar, known as clustering. So, a cluster is a collection of
objects that acquire nearly same attribute values. The property of an object in a cluster is
similar to other objects in same cluster but different with objects of other clusters.
Clustering is used in wide range of applications like pattern recognition, image processing,
data analysis, machine learning etc. Nowadays, more attention has been put on categorical
data rather than numerical data. Where, the range of numerical attributes organizes in a
class like small, medium, high, and so on. There is wide range of algorithm that used to
make clusters of given categorical data. Our approach is to enhance the working on well-
known clustering algorithm k-modes to improve accuracy of algorithm. We proposed a new
approach named “High Accuracy Clustering Algorithm for Categorical datasets”.
Brain tumor is a malformed growth of cells within brain which may be
cancerous or non-cancerous. The term ‘malformed’ indicates the existence of tumor. The
tumor may be benign or malignant and it needs medical support for further classification.
Brain tumor must be detected, diagnosed and evaluated in earliest stage. The medical
problems become grave if tumor is detected at the later stage. Out of various technologies
available for diagnosis of brain tumor, MRI is the preferred technology which enables the
diagnosis and evaluation of brain tumor. The current work presents various clustering
techniques that are employed to detect brain tumor. The classification involves classification
of images into normal and malformed (if detected the tumor). The algorithm deals with
steps such as preprocessing, segmentation, feature extraction and classification of MR brain
images. Finally, the confirmatory step is specifying the tumor area by technique called
region of interest.
A Proxy signature scheme enables a proxy signer to sign a message on behalf of
the original signer. In this paper, we propose ECDLP based solution for chen et. al [1]
scheme. We describe efficient and secure Proxy multi signature scheme that satisfy all the
proxy requirements and require only elliptic curve multiplication and elliptic curve addition
which needs less computation overhead compared to modular exponentiations also our
scheme is withstand against original signer forgery and public key substitution attack.
This document proposes a digital watermarking technique using LSB replacement with secret key insertion for enhanced data security. The technique works by inserting a watermark into the least significant bits of pixels in an image. A secret key is also inserted during transmission for additional security. The watermarked image is generated without noticeably impacting image quality. The proposed method was tested on sample images and successfully embedded watermarks while maintaining visual quality. The technique aims to provide copyright protection and authentication of digital images and documents.
Today among various medium of data transmission or storage our sensitive data
are not secured with a third-party, that we used to take help of. Cryptography plays an
important role in securing our data from malicious attack. This paper present a partial
image encryption based on bit-planes permutation using Peter De Jong chaotic map for
secure image transmission and storage. The proposed partial image encryption is a raw data
encryption method where bits of some bit-planes are shuffled among other bit-planes based
on chaotic maps proposed by Peter De Jong. By using the chaotic behavior of the Peter De
Jong map the position of all the bit-planes are permuted. The result of the several
experimental, correlation analysis and sensitivity test shows that the proposed image
encryption scheme provides an efficient and secure way for real-time image encryption and
decryption.
This paper presents a survey of Dependency Analysis of Service Oriented
Architecture (SOA) based systems. SOA presents newer aspects of dependency analysis due
to its different architectural style and programming paradigm. This paper surveys the
previous work taken on dependency analysis of service oriented systems. This study shows
the strengths and weaknesses of current approaches and tools available for dependency
analysis task in context of SOA. The main motivation of this work is to summarize the
recent approaches in this field of research, identify major issue and challenges in
dependency analysis of SOA based systems and motivate further research on this topic.
In this paper, proposed a novel implementation of a Soft-Core system using
micro-blaze processor with virtex-5 FPGA. Till now Hard-Core processors are used in
FPGA processor cores. Hard cores are a fixed gate-level IP functions within the FPGA
fabrics. Now the proposed processor is Soft-Core Processor, this is a microprocessor fully
described in software, usually in an HDL. This can be implemented by using EDK tool. In
this paper, developed a system which is having a micro-blaze processor is the combination
of both hardware & Software. By using this system, user can control and communicate all
the peripherals which are in the supported board by using Xilinx platform to develop an
embedded system. Implementing of Soft-Core process system with different peripherals like
UART interface, SPA flash interface, SRAM interface has to be designed using Xilinx
Embedded Development Kit (EDK) tools.
The article presents a simple algorithm to construct minimum spanning tree and
to find shortest path between pair of vertices in a graph. Our illustration includes the proof
of termination. The complexity analysis and simulation results have also been included.
Wimax technology has reshaped the framework of broadband wireless internet
service. It provides the internet service to unconnected or detached areas such as east South
Africa, rural areas of America and Asia region. Full duplex helpers employed with one of
the relay stations selection and indexing method that is Randomized Distributed Space Time
are used to expand the coverage area of primary Wimax station. The basic problem was
identified at cell edge due to weather conditions (rain, fog), insertion of destruction because
of multiple paths in the same communication channel and due to interference created by
other users in that communication. It is impractical task for the receiver station to decode
the transmitted signal successfully at the cell edges, which increases the high packet loss and
retransmissions. But Wimax is a outstanding technology which is used for improving the
quality of internet service and also it offers various services like Voice over Internet
Protocol, Video conferencing and Multimedia broadcast etc where a little delay in packet
transmission can cause a big loss in the communication. Even setup and initialization of
another Wimax station nearer to each other is not a good alternate, where any mobile
station can easily handover to another base station if it gets a strong signal from other one.
But in rural areas, for few numbers of customers, installation of base station nearer to each
other is costlier task. In this review article, we present a scheme using R-DSTC technique to
choose and select helpers (relay nodes) randomly to expand the coverage area and help to
mobile station as a helper to provide secure communication with base station. In this work,
we use full duplex helpers for better utilization of bandwidth.
Radio Frequency identification (RFID) technology has become emerging
technique for tracking and items identification. Depend upon the function; various RFID
technologies could be used. Drawback of passive RFID technology, associated to the range
of reading tags and assurance in difficult environmental condition, puts boundaries on
performance in the real life situation [1]. To improve the range of reading tags and
assurance, we consider implementing active backscattering tag technology. For making
mobiles of multiple radio standards in 4G network; the Software Defined Radio (SDR)
technology is used. Restrictions in Existing RFID technologies and SDR technology, can be
eliminated by the development and implementation of the Software Defined Radio (SDR)
active backscattering tag compatible with the EPC global UHF Class 1 Generation 2 (Gen2)
RFID standard. Such technology can be used for many of applications and services.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
Physiology and chemistry of skin and pigmentation, hairs, scalp, lips and nail, Cleansing cream, Lotions, Face powders, Face packs, Lipsticks, Bath products, soaps and baby product,
Preparation and standardization of the following : Tonic, Bleaches, Dentifrices and Mouth washes & Tooth Pastes, Cosmetics for Nails.
Assessment and Planning in Educational technology.pptxKavitha Krishnan
In an education system, it is understood that assessment is only for the students, but on the other hand, the Assessment of teachers is also an important aspect of the education system that ensures teachers are providing high-quality instruction to students. The assessment process can be used to provide feedback and support for professional development, to inform decisions about teacher retention or promotion, or to evaluate teacher effectiveness for accountability purposes.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
2. The section II and III explain about the data selection filters (Butterworth & Notch), and the features
extracted. The section IV gives the results and discussion and conclusion of the context.
Figure 1. Emg Signal Of Normal, Als, Myopathy Patient From Biceps Brachii Region
II. DATA SELECTION
A. Subjects
For our study, we have chosen 3 groups. First group consisted of 10 normal subjects aged 21-35 years, 4
females and 6 males. All of the 10 subjects are in very good physical condition except one. None in this
group had signs or history of neuromuscular disorders. The group with myopathy consisted of 7 patients; 2
females and 5 males aged 19-63 years. The ALS group consisted of 8 subjects; 4 females and 4 males aged
35-67 years. Besides clinical and electrophysiological signs compatible with ALS, 5 of them died within a
few years after onset of the disorder, supporting the diagnosis of ALS.
B. Experimental Protocol
The EMG signals were recorded under usual conditions for MUAP analysis. The recordings were made at
low (just above threshold) voluntary and constant level of contraction. A standard concentric needle electrode
was used. The EMG signals were recorded from bicep brachii of bicep. The high and low pass filters of the
EMG amplifier were set at 2 Hz and 10 kHz for 11msec duration with sampling frequency of 10 kHz [8].
Figure 1 shows plot of different types of sample signal where around 250 points of sample signals are taken.
III. PROPOSED METHOD
In this paper, we describe each of the following steps involved for the classification of the acquired EMG
signal. These steps are: (A) Base line shift for accurate estimation of parameters. (B) Butterworth band pass
Filter (C) Notch filter for removing DC power line interference. (D) Various parameters/features
calculation.(E) Classifier Design.
A. Base Line Shifting
Generally human being produces static current which will interfere while recording EMG signal. Due to this
problem EMG signal shifts upper side from the base line. This defect occurs due to the muscle tension, body
movement or some environmental noise.
B. Butterworth Filter
In this paper, we used Butterworth band pass filter which is a combination of low pass filter and high pass
filter. Here lower cut off frequency is 5 Hz and higher cut off frequency is 1500 Hz.
C. Notch Filter
Due to the DC power line interference a significant noise appears during EMG acquisition. That should be
removed by using Notch Filter at 50 Hz.
43
3. D. Feature Extraction & Calculation
In this paper, we are calculating different entropies for classifying above referred EMG signals. Entropies are
stated below:
Lyapunov Exponent: It is measure of rate of separation of infinitesimally close trajectories for dynamical
system. It gives the rate of exponential divergence from perturbed initial conditions. It is a measure of the
rate at which nearby trajectories in phase space diverge. . Thus a value of +1 means separation of nearby
orbits doubles on the average in the time interval between data samples represented by Eq. (1).
λ = lim
|∆
|∆ (
→
|→
|∆
, )|
|
(1)
The displayed error is estimated from 2.5 times the standard deviation of the slope divided by the square root
of the number of trajectories followed. It is given by following [6][12].
Capacity Dimension: It is also known as Hausdorff dimension. It is calculated successively dividing the
phase space with embedding dimension D into equal hyper cubes that are occupied with data points with
respect to the log of the normalized linear dimension of hypercubes. The capacity dimension of space X is a
real number dcapacity such that if n( ) denotes the minimum number of open sets of diameter less than or equal
to , then n( ) is proportional to ∈ as ∈→ 0. It is represented by Eq. (2).
= − lim∈→
(2)
∈
where
is the number of elements forming a finite Space and is a bound on the diameter of the sets
involved (informally, is the size of each element used to cover the set, which is taken to approach 0) if the
limit exists.
Correlation Dimension: It is a method to determine the dimension of space occupied by sets of random
points [9], [10]. It provides a good measure of the complexity of the dynamics, i.e. of the number of active
degrees of freedom. The correlation dimension is a characteristic of the underlying invariant measure µ. In a
certain sense it characterizes how smoothly µ is distributed over the attractor: if µ is a point measure, then α
= 0, and if µ is absolutely continuous with respect to Lebesgue measure, then α equals the topological
dimension d of X. These are two boundary cases, in general 0<α<d. This paper explains the correlation
dimension by Eq. (3).
C(r) = ∬ Ω (‖ − ‖ ≤ ) ( ) ( ) (3)
Ω
where I denote an indicator function.
Correlation Matrix: Singular Value decomposition begins with the correlation matrix which is a two
dimensional matrix formed by putting the value of the correlation function with tau=0 along the diagonal and
putting of the value of the correlation function with tau=1 to the right and left filled [11]. The Eigen values
are determined.
Probability Distribution Function: It shows the probability of occurrence of certain data values. We can
specify a number of bins between 2 and 128 into which data points are sorted according to their value. The
bins all have the same width and are spread uniformly between the lowest and highest data value. It is
represented by Eq. (4)
()
F(x) = F(x) = ∫
(4)
Hurst Exponent: It is referred to as the "index of dependence," and is the relative tendency of a time
series to either strongly regress to the mean or 'cluster' in a direction. The Hurst exponent is used as a
measure of the long term memory of time series, i.e. the autocorrelation of the time series as in Eq. (5).
( )
( )
where
[
( )
( )
=
→∞
(5)
] is the rescaled range.
E[x] is the expected value.
n is the time of the last observation
(e.g. it corresponds to Xn in the input time series).
C is a constant.
E. Support Vector Machine as a classifier
In this paper, after feature extraction, feature matrix is normalized in order to have features in a same range.
Simply, SVM classifier was trained by the train-train data and then used for classifying the train-test data.
44
4. SVM creates a separating hyper plane between two clusters of data, thus classifying data falling into two
different classes. In non-linear classification, same operation was performed by non-linear kernel function, in
our case, Gaussian-RBF kernel, given by Eq. (6).
,
= exp −
(
−
/2
)
(6)
In this work we take two different group i.e. normal and abnormal (Myopathy & Neuropathy) signal for the
classification purpose .We take 70% of signal to train the expert model using SVMStruct a structure with the
Kernel Function fields [4],[5]. Kernel function Value is Gaussian Radial Basis Function kernel (‘rbf’)
function handle with a scaling factor, sigma which is varying from 0 values to 10 and takes those which give
maximum accuracy.
IV. RESULT AND DISCUSSION
We are classifying EMG signals for differentiating different diseases from the normal. Our classification uses
features which has described above. So we are spotting some of the features which produce demarcation
between different subjects. Then we train and test the SVM classifier with various extracted features like for
EMG. The classifier was also cross validated by taking feature vector in different combinations
From the result we state that Lyapunov Exponent & Hurst Exponent is the best parameter to classify the
EMG signals when taken individually and the classification accuracy is around 93%. By taking the combined
features Hurst Exponent & Lyapunov Exponent gives us classification accuracy around 88%.
V. CONCLUSION
In this present study we are analyzing the different class of EMG signals considering some of the best nonlinear features. Based on the above features we can state that LLE and Hurst exponent are the best to
distinguish the EMG signals. As per the authors the techniques used is a cost effective and time effective
approach for analyzing the EMG signals. In future we will compare different machine expertise system for
classifying the EMGs for detecting the diseases.
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