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
Sciencedirect - Elsevier - Clinical Neurophysiology, 130 (2019) 145-154: doi:10:1016/j:clinph:2018:09:018
Kai Rui Wana,b, Tomasz Maszczyk c, Angela An Qi See a,b, Justin Dauwels c, Nicolas Kon Kam King a,b,d,⇑
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