Abstract
Nowadays, Deep Brain Stimulation (DBS) has been used in the surgical treatment of various neurological diseases, such as Essential Tremor (ET). For severe cases, surgical treatment with DBS can suppress the disease better than drug therapy. However, existing DBS devices require the physician to manually set the parameters frequently to achieve the desired therapeutic effect. In this case, adaptive DBS devices become a possible solution for this problem. Adaptive DBS equipment still faces some problems, such as the choice of implementation method, data security and how to ensure that it will not cause serious damage to human body in clinical trials. In this paper, three kinds of adaptive DBS are analyzed, including their mechanism and core algorithms. This paper can provide reference for those who study DBS devices, those who study the application of machine learning algorithms to adaptive devices, and those who want to know about adaptive DBS devices.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
