TY - GEN
T1 - Embedded Phase-Amplitude Coupling Based Closed-loop Platform for Parkinson's Disease
AU - Alexandre, M.
AU - Luan, S.
AU - Mari, Z.
AU - Anderson, W. S.
AU - Salimpour, Y.
AU - Constandinou, T. G.
AU - Grand, L. B.
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/20
Y1 - 2018/12/20
N2 - Deep Brain Stimulation (DBS) is a widely used clinical therapeutic modality to treat Parkinsons disease refractory symptoms and complications of levodopa therapy. Currently available DBSsystems 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 symptom 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.
AB - Deep Brain Stimulation (DBS) is a widely used clinical therapeutic modality to treat Parkinsons disease refractory symptoms and complications of levodopa therapy. Currently available DBSsystems 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 symptom 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.
UR - http://www.scopus.com/inward/record.url?scp=85060873089&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85060873089&partnerID=8YFLogxK
U2 - 10.1109/BIOCAS.2018.8584699
DO - 10.1109/BIOCAS.2018.8584699
M3 - Conference contribution
AN - SCOPUS:85060873089
T3 - 2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings
BT - 2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018
Y2 - 17 October 2018 through 19 October 2018
ER -