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

Highlights • Automatic STN detection improves the efficiency and accuracy of target location. • Both spike independent and dependent features are useful for detection of STN. • Further research required for STN sub territories. Abstract 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. [ABSTRACT FROM AUTHOR]