دانلود مقاله انگلیسی رایگان:مروری بر انتخاب ضبط میکروالکترود ویژگی های یادگیری ماشین در عمل جراحی تحریک عمیق مغز برای بیماری پارکینسون - 2019
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  • A review on microelectrode recording selection of features for machine learning in deep brain stimulation surgery for Parkinson’s disease A review on microelectrode recording selection of features for machine learning in deep brain stimulation surgery for Parkinson’s disease
    A review on microelectrode recording selection of features for machine learning in deep brain stimulation surgery for Parkinson’s disease

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    A review on microelectrode recording selection of features for machine learning in deep brain stimulation surgery for Parkinson’s disease


    ترجمه فارسی عنوان مقاله:

    مروری بر انتخاب ضبط میکروالکترود ویژگی های یادگیری ماشین در عمل جراحی تحریک عمیق مغز برای بیماری پارکینسون


    منبع:

    Sciencedirect - Elsevier - Clinical Neurophysiology, 130 (2019) 145-154: doi:10:1016/j:clinph:2018:09:018


    نویسنده:

    Kai Rui Wana,b, Tomasz Maszczyk c, Angela An Qi See a,b, Justin Dauwels c, Nicolas Kon Kam King a,b,d,⇑


    چکیده انگلیسی:

    Objective: This study seeks to systematically review the selection of features and algorithms for machine learning and automation in deep brain stimulation surgery (DBS) for Parkinson’s disease. This will assist in consolidating current knowledge and accuracy levels to allow greater understanding and research to be performed in automating this process, which could lead to improved clinical outcomes. Methods: A systematic literature review search was conducted for all studies that utilized machine learning and DBS in Parkinson’s disease. Results: Ten studies were identified from 2006 utilizing machine learning in DBS surgery for Parkinson’s disease. Different combinations of both spike independent and spike dependent features have been utilized with different machine learning algorithms to attempt to delineate the subthalamic nucleus (STN) and its surrounding structures. Conclusion: The state-of-the-art algorithms achieve good accuracy and error rates with relatively short computing time, however, the currently achievable accuracy is not sufficiently robust enough for clinical practice. Moreover, further research is required for identifying subterritories of the STN. Significance: This is a comprehensive summary of current machine learning algorithms that discriminate the STN and its adjacent structures for DBS surgery in Parkinson’s disease.
    Keywords: Parkinson’s disease | Machine learning | Microelectrode recording | Automation


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 10
    حجم فایل: 1022 کیلوبایت

    قیمت: رایگان


    توضیحات اضافی:




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