Fig 6
SVM polynomial kernel model confusion matrix representing 95% of classification accuracy Fig. 7. SVM RBF kernel model confusion matrix representing 88.3% of classification accuracy
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Implementation of Artificial intelligence techniques is used as a medical diagnostic tool to increase the diagnostic accuracy and provide more additional knowledge. Muscular dystrophy is a disorder which diagnosed with Electromyography (EMG) signals. A Waveletbased decomposition technique is proposed here to classified Healthy EMG signals (Normal)...
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Context 1
... 2 shows the SVM polynomial kernel function results for different poly-order. Figure 6 shows the SVM polynomial kernel model confusion matrix representing 95% of classification accuracy. Table 4 depicts the best classification accuracy using the SVM model (For polynomial kernel model). ...
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... Their findings indicated that the SVM surpassed the other two classifiers with an accuracy of 95 %. In the research of Kehri and Awale et al. [28], the SVM classifier achieved an accuracy of 95 % compared to an ANN classifier. ...
The control of human-machine interfaces (HMIs), such as motorized wheelchairs, has been widely investigated using biopotentials produced by electrochemical processes in the human body. However, many studies in this field sometimes overlook crucial factors like special users' needs, who often have inadequate muscle mass and strength, and paresis needed to operate a wheelchair. This study proposes a novel solution: an economical, universally compatible, and user-centric manual-to-powered wheelchair conversion kit. The powered wheelchair is operated using a hybrid control system integrating electroencephalogram (EEG) and electromyography (EMG), utilizing an LSTM network. It uses a low-cost electroencephalogram (EEG) headset and a wearable electromyography (EMG) electrode armband to solve these constraints. The proposed system comprised three crucial objectives: the development of an EEG-based user attentive detection system, an EMG-based navigation system, and a transform conventional wheelchair into a powered wheelchair. Human test subjects were utilized to evaluate the proposed system, and the study complied with accepted ethical guidelines. We selected four EEG features (p < 0.023) for the attentive detection system and six EMG features (p < 0.037) to detect navigation intentions. User attentive detection was achieved at 83.33 (±0.34) %, while the navigation intention system produced 86.67 (±0.52) % accuracy. The overall system was successful in reaching an accuracy rate of 85.0 (±0.19) % and a weighted average precision of 0.89. After the dataset was trained using an LSTM network, the overall accuracy produced was 97.3 (±0.5) %, higher than the accuracy produced by the Quadratic SVM classifier. By giving older and disabled people a more convenient way to use powered wheelchairs, this research helps to build ergonomic and cost-effective biopotential-based HMIs, enhancing their quality of life.
... It plays a significant role in the diagnosis of neuromuscular disorders [61]. Studies have shown that signal processing methods (such as Wavelet Transform [62][63][64], Hilbert-Huang transform [65]) and machine learning techniques (such as KNN, decision trees, SVM [66,67], Etc.) can diagnose the etiology of gait disorders with up to 99% accuracy [65,66]. ...
... This information can be critical in making diagnoses and determining appropriate treatment options for patients. EMG monitoring can help detect abnormalities in the electrical activity of skeletal muscles, which can indicate conditions such as muscular dystrophy or nerve damage [36,37]. This information can also be used to guide treatment options and track the progression of these conditions. ...
The technology of wearable medical equipment has advanced to the point where it is now possible to monitor the electrocardiogram and electromyogram comfortably at home. The transition from wet Ag/AgCl electrodes to various types of gel-free dry electrodes has made it possible to continuously and accurately monitor the biopotential signals. Fabrics or textiles, which were once meant to protect the human body, have undergone significant development and are now employed as intelligent textile materials for healthcare monitoring. The conductive textile electrodes provide the benefit of being breathable and comfortable. In recent years, there has been a significant advancement in the fabrication of wearable conductive textile electrodes for monitoring biopotential signals. This review paper provides a comprehensive overview of the advances in wearable conductive textile electrodes for biopotential signal monitoring. The paper covers various aspects of the technology, including the electrode design, various manufacturing techniques utilised to fabricate wearable smart fabrics, and performance characteristics. The advantages and limitations of various types of textile electrodes are discussed, and key challenges and future research directions are identified. This will allow them to be used to their fullest potential for signal gathering during physical activities such as running, swimming, and other exercises while being linked into wireless portable health monitoring systems.
... Subsequently, the best combination is selected to form a matrix of features that will be used in the classification stage. Finally, in the classification section, various algorithms are used, including neural networks, decision trees, linear discriminant analysis, and support vector machines (SVM), among others [4,5]. ...
Electromyography (EMG) processing is a fundamental part of medical research. It offers the possibility of developing new devices and techniques for the diagnosis, treatment, care, and rehabilitation of patients, in most cases non-invasively. However, EMG signals are random, non- stationary, and non-linear, making their classification difficult. Due to this, it is of vital importance to define which factors are helpful for the classification process. In order to improve this process, it is possible to apply algorithms capable of identifying which features are most important in the categorization process. Algorithms based on metaheuristic methods have demonstrated an ability to search for suitable subsets of features for optimization problems. Therefore, this work proposes a methodology based on genetic algorithms for feature selection to find the parameter space that offers the slightest classification error in 250 ms signal segments. For classification, a support vector machine is used. For this work, two databases were used, the first corresponding to the right upper extremity and the second formed by movements of the right lower extremity. For both databases, a feature space reduction of over 65% was obtained, with a higher average classification efficiency of 91% for the best subset of parameters. In addition, particle swarm optimization (PSO) was applied based on right upper extremity data, obtaining an 88% average error and a 46% reduction for the best subset of parameters. Finally, a sensitivity analysis was applied to the characteristics selected by PSO and genetic algorithms for the database of the right upper extremity, obtaining that the parameters determined by the genetic algorithms show greater sensitivity for the classification process.
... Electromyography (EMG) signals are generated from the muscle cells and are measured in electric potentials [1] [13]. EMG based diagnosis for neuromuscular disorders is reliable since it denotes the difference of electric potentials of the electrodes which is generated by the subject muscle cells while performing movements [1] [2]. ...
... Electromyography (EMG) signals are generated from the muscle cells and are measured in electric potentials [1] [13]. EMG based diagnosis for neuromuscular disorders is reliable since it denotes the difference of electric potentials of the electrodes which is generated by the subject muscle cells while performing movements [1] [2]. Myopathy is a non-progressive neuromuscular disorder which affects the muscle cells. ...
... Kehri et al [1], presents an EMG signal analysis for the diagnosis of myopathy and neuropathy. The analysis focuses on decomposition of signals using wavelet transform (WT). ...
Myopathy and Neuropathy are non-progressive and progressive neuromuscular disorders which weakens the muscles and nerves respectively. Electromyography (EMG) signals are bio signals obtained from the individual muscle cells. EMG based diagnosis for neuromuscular disorders is a safe and reliable method. Integrating the EMG signals with machine learning techniques improves the diagnostic accuracy. The proposed system performs analysis on the clinical raw EMG dataset which is obtained from the publicly available PhysioNet database. The two-channel raw EMG dataset of healthy, myopathy and neuropathy subjects are divided into samples. The Time Domain (TD) features are extracted from divided samples of each subject. The extracted features are annotated with the class label representing the state of the individual. The annotated features split into training and testing set in the standard ratio 70: 30. The comparative classification analysis on the complete annotated features set and prominent features set procured using Pearson correlation technique is performed. The features are scaled using standard scaler technique. The analysis on scaled annotated features set and scaled prominent features set is also implemented. The hyperparameter space of the classifiers are given by trial and error method. The hyperparameters of the classifiers are tuned using Bayesian optimization technique and the optimal parameters are obtained. and are fed to the tuned classifier. The classification algorithms considered in the analysis are Random Forest and Multi-Layer Perceptron Neural Network (MLPNN). The performance evaluation of the classifiers on the test data is computed using the Accuracy, Confusion Matrix, F1 Score, Precision and Recall metrics. The evaluation results of the classifiers states that Random Forest performs better than MLPNN wherein it provides an accuracy of 96 % with non-scaled Time Domain (TD) features and MLPNN outperforms better than Random Forest with an accuracy of 97% on scaled Time Domain (TD) features which is higher than the existing systems. The inferences from the evaluation results is that Bayesian optimization tuned classifiers improves the accuracy which provides a robust diagnostic model for neuromuscular disorder diagnosis.
... Some authors consider that there is noise also in higher frequencies, and thus they use filters that cancel up to 20 Hz [2,6,8,13,[15][16][17]19,21,23,24]. Nevertheless, in [7], the authors suppressed frequencies between 90 and 250 Hz and, in [25], the authors removed frequencies lower than 5 Hz and higher than 375 Hz. Table 1 summarizes the band-pass frequency allowed by each study. ...
... Sampling Frequency [15,[32][33][34] 500 Hz [1,9,10,[12][13][14][17][18][19][20][21][27][28][29] 1 kHz [2] 1.5 kHz [7,8,11,16,30,[35][36][37] 2 kHz [22] 3 kHz [6,25,26] 4 kHz [31] 10 kHz ...
... Number of Channels [25] 1 [16,17,20,26] 2 [24,31] 3 [1,[8][9][10]13,21,23,29,35,39,[44][45][46] 4 [19,36,40,41] 6 [2,7,11,15,22,30,32,34] 8 [37] 12 [33] 14 [12,14] 16 [47] 22 Table 4. Electrodes type and place of electrode placement body. ...
This paper gives an overview of the different research works related to electromyographic signals (EMG) classification based on Support Vector Machines (SVM). The article summarizes the techniques used to make the classification in each reference. Furthermore, it includes the obtained accuracy, the number of signals or channels used, the way the authors made the feature vector, and the type of kernels used. Hence, this article also includes a compilation about the bands used to filter signals, the number of signals recommended, the most commonly used sampling frequencies, and certain features that can create the characteristics of the vector. This research gathers articles related to different kinds of SVM-based classification and other tools for signal processing in the field.
... One way to cope with this problem to incorporate feedback structure in the networks to provide them with an ability to memorize incoming time-varying information. Incorporating feedback structure in feedforward networks are results in so-called LRNNs, which have feedback connections between units of different layers or connections of self-loop type [24][25][26][27][28]. This paper contains an introduction and general concepts, background of EMG signal physiology of signal, fundamentals of signal, measurement system of real EMG human muscle signals, and design of detection system based on EMG signal by using LRNNs and taking the human arm as case study. ...
Muscle diseases occur in all age groups and can cause serious physical disability. The effect of such diseases is severe when children and young adults are affected. The needs of these patients are numerous and complicated and are frequently inadequately met. Some muscle diseases respond well to medical treatment, whereas many physical disabilities can be improved or prevented. This paper presents the implement of The Locally Recurrent Neural Networks (LRNNs) to detect the muscles diseases based on real measurement of electromyography (EMG) signals. The LRNNs training achieved by using Levenberg-Marquardt back propagation (LMBP) to get high accuracy through the identification and recognition process. Many processing applied with EMG signal through LRNNs before recognition the diseases. Finally design the simulation of this work by using Graphical User Interface (GUI) through MATLAB. Satisfactory results are obtained with the case study of real implement on human arm muscles.
