Embedded phase-amplitude coupling based closed-loop platform for Parkinson's Disease
File(s)
Accepted version
Author(s)
Type
Conference Paper
Abstract
Deep Brain Stimulation (DBS) is a widely used clin-
ical therapeutic modality to treat Parkinsons disease refractory
symptoms and complications of levodopa therapy. Currently
available DBS systems use continuous, open-loop stimulation
strategies. It might be redundant and we could extend the battery
life otherwise. Recently, robust electrophysiological signatures
of Parkinsons disease have been characterized in motor cortex
of patients undergoing DBS surgery. Reductions in the beta-
gamma Phase-Amplitude coupling (PAC) correlated with symp-
tom improvement, and the therapeutic effects of DBS itself. We
aim to develop a miniature, implantable and adaptive system,
which only stimulates the neural target, when triggered by the
output of the appropriate PAC algorithm. As a first step, in this
paper we compare published PAC algorithms by using human
data intra-operatively recorded from Parkinsonian patients. We
then introduce IIR masking for later achieving fast and low-
power FPGA implementation of PAC mapping for intra-operative
studies. Our closed-loop application is expected to consume
significantly less power than current DBS systems, therefore
we can increase the battery life, without compromising clinical
benefits.
ical therapeutic modality to treat Parkinsons disease refractory
symptoms and complications of levodopa therapy. Currently
available DBS systems use continuous, open-loop stimulation
strategies. It might be redundant and we could extend the battery
life otherwise. Recently, robust electrophysiological signatures
of Parkinsons disease have been characterized in motor cortex
of patients undergoing DBS surgery. Reductions in the beta-
gamma Phase-Amplitude coupling (PAC) correlated with symp-
tom improvement, and the therapeutic effects of DBS itself. We
aim to develop a miniature, implantable and adaptive system,
which only stimulates the neural target, when triggered by the
output of the appropriate PAC algorithm. As a first step, in this
paper we compare published PAC algorithms by using human
data intra-operatively recorded from Parkinsonian patients. We
then introduce IIR masking for later achieving fast and low-
power FPGA implementation of PAC mapping for intra-operative
studies. Our closed-loop application is expected to consume
significantly less power than current DBS systems, therefore
we can increase the battery life, without compromising clinical
benefits.
Date Issued
2018-12-24
Date Acceptance
2018-08-13
Citation
2018 IEEE Biomedical Circuits and Systems Conference (BioCAS), 2018, pp.527-530
ISSN
2163-4025
Publisher
IEEE
Start Page
527
End Page
530
Journal / Book Title
2018 IEEE Biomedical Circuits and Systems Conference (BioCAS)
Replaces
10044/1/63455
Copyright Statement
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor
Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (EPSRC)
Grant Number
EP/M020975/1
EP/N002474/1
Source
IEEE Biomedical Circuits and Systems (BioCAS) Conference
Subjects
Science & Technology
Technology
Computer Science, Information Systems
Engineering, Biomedical
Engineering, Electrical & Electronic
Computer Science
Engineering
DEEP BRAIN-STIMULATION
OSCILLATIONS
Publication Status
Published
Start Date
2018-10-17
Finish Date
2018-10-19
Coverage Spatial
Cleveland, Ohio, USA
Date Publish Online
2018-10-17