TY - GEN
T1 - Electrophysiological signal processing for intraoperative localization of subthalamic nucleus during deep brain stimulation surgery
AU - Khosravi, Mahsa
AU - Atashzar, Seyed Farokh
AU - Gilmore, Greydon
AU - Jog, Mandar S.
AU - Patel, Rajni V.
N1 - Funding Information:
This research was supported by the Natural Sciences and Engineering Research Council (NSERC) of Canada under the NSERC Discovery Grant RGPIN 1345; the Canadian Institutes of Health Research (CIHR) and NSERC under a Collaborative Health Research Projects (CHRP) Grant #316170; the AGEWELL Network of Centres of Excellence Grant AW CRP 2015-WP5.3.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - In this paper, a novel technique is proposed for localization of Subthalamic Nucleus (STN) during deep brain stimulation (DBS) Surgery. DBS surgery is performed on individuals living with Parkinson's disease (PD) to permanently implant stimulation electrodes for managing some motor symptoms of PD. The most challenging part of this surgery is to accurately place the electrodes inside the STN. Commonly, microelectrode recordings (MERs) are interpreted by the surgical team intraoperatively to estimate the location of electrodes and detect the borders of the STN. In this work, we aim to automate the process of localizing the STN using a machine learning technique (trained based on the electrophysiological signals that we have collected during 20 surgeries). The proposed approach is capable of detecting the dorsal borders of the STN during the procedure with high accuracy (85%), and outperforms the current state-of-the-art approach for this application.
AB - In this paper, a novel technique is proposed for localization of Subthalamic Nucleus (STN) during deep brain stimulation (DBS) Surgery. DBS surgery is performed on individuals living with Parkinson's disease (PD) to permanently implant stimulation electrodes for managing some motor symptoms of PD. The most challenging part of this surgery is to accurately place the electrodes inside the STN. Commonly, microelectrode recordings (MERs) are interpreted by the surgical team intraoperatively to estimate the location of electrodes and detect the borders of the STN. In this work, we aim to automate the process of localizing the STN using a machine learning technique (trained based on the electrophysiological signals that we have collected during 20 surgeries). The proposed approach is capable of detecting the dorsal borders of the STN during the procedure with high accuracy (85%), and outperforms the current state-of-the-art approach for this application.
KW - DBS surgery
KW - Electrophysiological Signal Processing
KW - Parkinson's disease.
KW - STN localization
KW - Supervised machine learning
UR - http://www.scopus.com/inward/record.url?scp=85063108174&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85063108174&partnerID=8YFLogxK
U2 - 10.1109/GlobalSIP.2018.8646606
DO - 10.1109/GlobalSIP.2018.8646606
M3 - Conference contribution
AN - SCOPUS:85063108174
T3 - 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings
SP - 424
EP - 428
BT - 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018
Y2 - 26 November 2018 through 29 November 2018
ER -