6 articles for “Electromyogram”

Set a search alert

Research ArticleOpen accessEvery word in the titleDetermination of Neuromuscular Diseases Using Complexity of Electromyogram SignalsKawser Ahammed, Mosabber Uddin AhmedCharacterization of Electromyogram (EMG) signals is important for identifying neuromuscular diseases. Although various techniques were implemented for classification, none of them were implemented in the complexity domain. In this study, characterization of EMG signals recorded from healthy, neuropathic and myopathic subjects has been performed in complexity domain based on Multiscale Entropy (MSE) method. To do this, the multiscale entropy method … …Neuroscience International, Vol. 10, Issue 1 · 7 Dec 2019 · 7,931 accessesView PDF Cite

Research ArticleOpen accessEvery word in the titleAdvances in Electromyogram Signal Classification to Improve the Quality of Life for the Disabled and Aged PeopleMd. R. Ahsan, Muhammad I. Ibrahimy, Othman O. KhalifaProblem statement: The social demands for the Quality Of Life (QOL) are increasing with the exponentially expanding silver generation. To improve the QOL of the disabled and elderly people, robotic researchers and biomedical engineers have been trying to combine their techniques into the rehabilitation systems. Various biomedical signals (biosignals) acquired from a specialized tissue, organ, or cell system like the … …Journal of Computer Science, Vol. 6, Issue 7 · 31 Jul 2010 · 8,813 accessesView PDF Cite

Research ArticleOpen accessIn the keywordsDesign and Implementation of Myoelectric Controlled ArmTariq M. Younes, Mohammad A. AlKhedher, Abdel-Hamid Soliman, Aiman Al AlawinIn this study a discrimination system, using a neural network for Electromyogram (EMG) externally controlled Arm is proposed. In this system, the Artificial Neural Network (ANN) is used to learn the relation between the power spectrum of EMG signal analysed by Fast Fourier Transform (FFT) and the performance desired by handicapped people. The Neural Network can discriminate 4 performances of … …Journal of Mechatronics and Robotics, Vol. 3, Issue 1 · 16 Sep 2019 · 6,281 accessesView PDF Cite

Research ArticleOpen accessIn the abstractThe Manifestation of the Discrete and Continuous Types of Bioelectric Control of Natural Locomotion at the Over-Five AgeKazbek Chermit, Anatoliy Zabolotniy, Angelina ShakhanovaThe concept of physiology of activity by N.A. Bernstein, developed on the grounds of in-depth theoretical and empirical analysis of natural human locomotion, served as a basis for understanding the target determination of human behavior, the mechanisms of motor skills formation and levels of building movements. Scientific and technological progress has determined the emergence of new methods for registration of … …American Journal of Applied Sciences, Vol. 13, Issue 5 · 27 May 2016 · 7,098 accessesView PDF Cite

Research ArticleOpen accessIn the abstractComparative Evaluation of Adaptive Filter and Neuro-Fuzzy Filter in Artifacts Removal From Electroencephalogram SignalPaulchamy Balaiah, IlavennilaProblem statement: This study presents an effective method for removing mixed artifacts (EOG-Electro-ocular gram, ECG-Electrocardiogram, EMG-Electromyogram) from the EEG-Electroencephalogram records. The noise sources increases the difficulty in analyzing the EEG and obtaining clinical information. EEG signals are multidimensional, non-stationary (i.e., statistical properties are not invariant in time), time domain biological signals, which are not reproducible. It is supposed to contain … …American Journal of Applied Sciences, Vol. 9, Issue 10 · 15 Aug 2012 · 7,087 accessesView PDF Cite

Research ArticleOpen accessIn the abstractA New Self-Adaptive Neuro Fuzzy Inference System for the Removal of Non-Linear Artifacts from the Respiratory SignalA. Bhavani Sankar, D. Kumar, K. SeethalakshmiProblem statement: In this study, a new ANFIS-based adaptive filter is proposed to remove the non-linear artifacts from the respiratory signal measured using MEMS based accelerometer sensor. The data recorded from the abdomen movement includes the respiratory signal, electromyogram, 50Hz power line interference and the random electrode noise. In order to avoid convergence into local extremes, the system employs ANFIS … …Journal of Computer Science, Vol. 8, Issue 5 · 16 Feb 2012 · 6,891 accessesView PDF Cite