[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Murugan et al., 2014 - Google Patents

EMG signal classification using ANN and ANFIS for neuro-muscular disorders

Murugan et al., 2014

Document ID
811656218228274386
Author
Murugan P
Varghese S
Publication year
Publication venue
International Journal of Biomedical Engineering and Technology

External Links

Snippet

Neuro-muscular disorders can be caused by immunological and autoimmune disorders. Electromyography (EMG) may aid with the diagnosis of nerve compression, nerve rest injury, with other problems of muscles and nerves. Electromyography signal studies the electrical …
Continue reading at www.inderscienceonline.com (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F3/00Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/011Arrangements for interaction with the human body, e.g. for user immersion in virtual reality
    • G06F3/015Input arrangements based on nervous system activity detection, e.g. brain waves [EEG] detection, electromyograms [EMG] detection, electrodermal response detection
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/04Architectures, e.g. interconnection topology
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/06Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/04Detecting, measuring or recording bioelectric signals of the body of parts thereof
    • A61B5/0476Electroencephalography
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/04Detecting, measuring or recording bioelectric signals of the body of parts thereof
    • A61B5/0402Electrocardiography, i.e. ECG
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235Details of waveform analysis
    • A61B5/7264Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/04Detecting, measuring or recording bioelectric signals of the body of parts thereof
    • A61B5/0488Electromyography
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/40Detecting, measuring or recording for evaluating the nervous system
    • A61B5/4076Diagnosing or monitoring particular conditions of the nervous system
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass

Similar Documents

Publication Publication Date Title
EP3836836B1 (en) Real-time spike detection and identification
Gazzoni et al. Quantifying forearm muscle activity during wrist and finger movements by means of multi-channel electromyography
Naik et al. Nonnegative matrix factorization for the identification of EMG finger movements: Evaluation using matrix analysis
Naik et al. Hand gestures for HCI using ICA of EMG
Sanchez et al. Brain-machine interface engineering
Bai et al. Upper Arm Motion High‐Density sEMG Recognition Optimization Based on Spatial and Time‐Frequency Domain Features
Serdar Bascil et al. Multi-channel EEG signal feature extraction and pattern recognition on horizontal mental imagination task of 1-D cursor movement for brain computer interface
Fang et al. Improve inter-day hand gesture recognition via convolutional neural network-based feature fusion
Andrade et al. An EEG brain-computer interface to classify motor imagery signals
Tosin et al. SEMG-based upper limb movement classifier: Current scenario and upcoming challenges
Vasanthi et al. Performance evaluation of pattern recognition networks using electromyography signal and time-domain features for the classification of hand gestures
Gabardi et al. A multi-artifact EEG denoising by frequency-based deep learning
Wu et al. A new EMG decomposition framework for upper limb prosthetic systems
Thanigaivelu et al. Oisvm: Optimal incremental support vector machine-based eeg classification for brain-computer interface model
Dzitac et al. Identification of ERD using fuzzy inference systems for brain-computer interface
Simar et al. Machine learning for hand pose classification from phasic and tonic EMG signals during bimanual activities in virtual reality
Murugan et al. EMG signal classification using ANN and ANFIS for neuro-muscular disorders
Miah et al. Prediction of motor imagery tasks from multi-channel eeg data for brain-computer interface applications
Kan et al. A Novel PSO‐Based Optimized Lightweight Convolution Neural Network for Movements Recognizing from Multichannel Surface Electromyogram
Al Ajrawi et al. A hierarchical recursive feature elimination algorithm to develop brain computer interface application of user behavior for statistical reasoning and decision making
Guerrero-Mendez et al. Enhancing complex upper-limb motor imagery discrimination through an incremental training strategy
Jana et al. A hybrid method for classification of physical action using discrete wavelet transform and artificial neural network
Lee et al. Iteratively calibratable network for reliable EEG-based robotic arm control
Waris et al. Classification of functional motions of hand for upper limb prosthesis with surface electromyography
Naik et al. Real-time hand gesture identification for human computer interaction based on ICA of surface electromyogram