@article{MAKHILLJEAS201914517536,
    title = {Intelligent System for Electromyography (EMG) Signals Classification},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {14},
    number = {5},
    pages = {1564-1570},
    year = {2019},
    issn = {1816-949x},
    doi = {jeasci.2019.1564.1570},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2019.1564.1570},
    author = {Mahmood and},
    keywords = {EMG signal,muscular disease,signal processing,DWT,SVM,extraction},
    abstract = {Muscles in the hands of the human are one of the important parts that depend on the performance
of hand dutiest hrough movement which helps the man in the performance of daily functions which carry things
and touch and feel everything around him. Muscle signals extracted by EMG system are used to diagnose
muscle signals and classify them as normal or abnormal. The process of detecting and classifying EMG signals
are difficult and exhausting process and require effort by the specialist doctor to diagnose them. Muscle injury
treatment is complicated and require surgical intervention when the injury is severe but when early detection
of injury may be treated without surgical intervention. In this study will use the EMG signalsthat have been
collected as data. At the beginning extracting, the signals by tracking each signal. These signals are then
analyze dusing DWT for muscle bands extraction that is used in feature extraction and using SVM for
classification. DWT used for tofeature extraction.}
    }