We have located links that may give you full text access.
A review on microelectrode recording selection of features for machine learning in deep brain stimulation surgery for Parkinson's disease.
Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology 2019 January
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.
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.
Full text links
Related Resources
Trending Papers
British Society of Gastroenterology guidelines for the management of hepatocellular carcinoma in adults.Gut 2024 April 17
Systemic lupus erythematosus.Lancet 2024 April 18
Should renin-angiotensin system inhibitors be held prior to major surgery?British Journal of Anaesthesia 2024 May
Ventilator Waveforms May Give Clues to Expiratory Muscle Activity.American Journal of Respiratory and Critical Care Medicine 2024 April 25
Acute Kidney Injury and Electrolyte Imbalances Caused by Dapagliflozin Short-Term Use.Pharmaceuticals 2024 March 27
Colorectal polypectomy and endoscopic mucosal resection: European Society of Gastrointestinal Endoscopy (ESGE) Guideline - Update 2024.Endoscopy 2024 April 27
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app
All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.
By using this service, you agree to our terms of use and privacy policy.
Your Privacy Choices
You can now claim free CME credits for this literature searchClaim now
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app