Assessment of electromyograms using genetic algorithm and artificial neural networks

Bakiya, A.; Kirshnamurthy, Kamalanand; Rajinikanth, V.

doi:10.1007/s12065-018-0174-0

Cited by 12 publications

(4 citation statements)

References 30 publications

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“…Recently, many researchers focus on building the model for classifying EMG signals using computational and knowledge engineering techniques such as linear discriminant analysis, logistic regression, K -means, KNN classifiers, support vector machine, extreme learning machines, artificial neural network, and deep learning methods [ 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Classification of Myopathy and Amyotrophic Lateral Sclerosis Electromyograms Using Bat Algorithm and Deep Neural Networks

Bakiya

Anitha²,

Sridevi³

et al. 2022

Behavioural Neurology

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Electromyograms (EMG) are a recorded galvanic action of nerves and muscles which assists in diagnosing the disorders associated with muscles and nerves. The efficient discrimination of abnormal EMG signals, myopathy and amyotrophic lateral sclerosis, engage crucial role in automatic diagnostic assistance tools, since EMG signals are nonstationary signals. Hence, for computer-aided identification of abnormalities, extraction of features, selection of superlative feature subset, and developing an efficient classifier are indispensable. Initially, time domain and Wigner-Ville transformed time-frequency features were extracted from abnormal EMG signals for experiments. The selection of substantial characteristics from time and time-frequency features was performed using bat algorithm. Extensively, deep neural network classifier is modelled for selected feature subset using bat algorithm from extracted time and time-frequency features. The performance of deep neural network exerting selected features from bat algorithm was compared with conventional artificial neural network. Results demonstrate that the deep neural network modelled with layers 2 and 3 ( neurons = 2 and 4) using time domain features is efficient in classifying the abnormalities of EMG signals with an accuracy, sensitivity, and specificity of 100% and also exhibited finer performance. Correspondingly, the developed conventional single layer artificial neural network ( neurons = 7 ) with time domain features has shown an accuracy of 83.3%, sensitivity of 100%, and specificity of 71.42%. The work materializes the significance of conventional and deep neural network using time and time-frequency features in diagnosing the abnormal signals exists in neuromuscular system using efficient classification.

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Section: Introductionmentioning

confidence: 99%

Classification of Myopathy and Amyotrophic Lateral Sclerosis Electromyograms Using Bat Algorithm and Deep Neural Networks

Bakiya

Anitha²,

Sridevi³

et al. 2022

Behavioural Neurology

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“…To optimize the performance of the ML model proposed by the authors, an ANN with a hidden layer of 10 neurons and tan sigmoid activation function combined with a subset containing the most suitable combination of 15 features (from a finite set of possibilities) was defined through a genetic algorithm (GA), implemented with the KNN classifier to carry out fitness evaluation of the different combinations of the GA population. The strategy proposed by Ambikapathy et al [ 43 ] to aid diagnosis using ANN obtained promising and statistically significant results in the process of classifying individuals between HC and ALS (86.6% Acc, 86.6% Spe, 86.6% Sen), HC, ALS or myopathy (82.2% Acc, 81.89% Sen, 91.31% Spe), and ALS or myopathy (96.6% Acc, 93.7% Sen, 100% Spe).…”

Section: Resultsmentioning

confidence: 99%

Biomedical signals and machine learning in amyotrophic lateral sclerosis: a systematic review

et al. 2021

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Introduction The use of machine learning (ML) techniques in healthcare encompasses an emerging concept that envisages vast contributions to the tackling of rare diseases. In this scenario, amyotrophic lateral sclerosis (ALS) involves complexities that are yet not demystified. In ALS, the biomedical signals present themselves as potential biomarkers that, when used in tandem with smart algorithms, can be useful to applications within the context of the disease. Methods This Systematic Literature Review (SLR) consists of searching for and investigating primary studies that use ML techniques and biomedical signals related to ALS. Following the definition and execution of the SLR protocol, 18 articles met the inclusion, exclusion, and quality assessment criteria, and answered the SLR research questions. Discussions Based on the results, we identified three classes of ML applications combined with biomedical signals in the context of ALS: diagnosis (72.22%), communication (22.22%), and survival prediction (5.56%). Conclusions Distinct algorithmic models and biomedical signals have been reported and present promising approaches, regardless of their classes. In summary, this SLR provides an overview of the primary studies analyzed as well as directions for the construction and evolution of technology-based research within the scope of ALS.

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“…Step 4: Termination: The Attribute selection process is terminated while the desired rows are selected and categorized as pain and normal signal [17,18].…”

Section: Figure 3 Ga Processmentioning

confidence: 99%

EMG Signal Feature Extraction, Normalization and Classification for Pain and Normal Muscles Using Genetic Algorithm and Support Vector Machine

Jain¹,

Garg²

2020

RIA

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Electromyography (EMG) is the process of measuring neuromuscular activities generated during the contraction and expansion period of muscles throughout the body. The potential is recorded by inserting needle or by placing electrodes on the surface of body. In this research, an automatic EMG signal classification system is developed using machine learning oriented Support Vector Machine (SVM). The collected data is selected using Genetic Algorithm (GA). The purpose of GA is to select those rows from the dataset, which contains potential or electrical activities recorded while the patient is in motion. Furthermore, the selected features are neutralized using critic method. To improve the row selection cosine similarity is being used to determine an average value hence also helps for data reduction. Based on the average similarity values, SVM is trained and used for classification during the testing phase. The experiment has been performed in MATLAB tool and the classification accuracy for normal and pain EMG signal of 91.3% and 92.4% respectively is achieved.

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Assessment of electromyograms using genetic algorithm and artificial neural networks

Cited by 12 publications

References 30 publications

Classification of Myopathy and Amyotrophic Lateral Sclerosis Electromyograms Using Bat Algorithm and Deep Neural Networks

Classification of Myopathy and Amyotrophic Lateral Sclerosis Electromyograms Using Bat Algorithm and Deep Neural Networks

Biomedical signals and machine learning in amyotrophic lateral sclerosis: a systematic review

EMG Signal Feature Extraction, Normalization and Classification for Pain and Normal Muscles Using Genetic Algorithm and Support Vector Machine

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