Universität zu Köln

Tan, Huiling ORCID: 0000-0001-8038-3029, Debarros, Jean, He, Shenghong, Pogosyan, Alek, Aziz, Tipu Z., Huang, Yongzhi ORCID: 0000-0002-2503-1589, Wang, Shouyan, Timmermann, Lars, Visser-Vandewalle, Veerle, Pedrosa, David J., Green, Alexander L. and Brown, Peter (2019). Decoding voluntary movements and postural tremor based on thalamic LFPs as a basis for closed-loop stimulation for essential tremor. Brain Stimul., 12 (4). S. 858 - 868. NEW YORK: ELSEVIER SCIENCE INC. ISSN 1876-4754

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Abstract

Background: High frequency Deep brain stimulation (DBS) targeting motor thalamus is an effective therapy for essential tremor (ET). However, conventional continuous stimulation may deliver unnecessary current to the brain since tremor mainly affects voluntary movements and sustained postures in ET. Objective: We aim to decode both voluntary movements and the presence of postural tremor from the Local field potentials (LFPs) recorded from the electrode implanted in motor thalamus for stimulation, in order to close the loop for DBS so that stimulation could be delivered on demand, without the need for peripheral sensors or additional invasive electrodes. Methods: LFPs from the motor thalamus, surface electromyographic (EMG) signals and/or behavioural measurements were simultaneously recorded in seven ET patients during temporary lead externalisation 3-5 days after the first surgery for DBS when they performed different voluntary upper limb movements. Nine different patients were recorded during the surgery, when they were asked to lift their arms to trigger postural tremor. A machine learning based binary classifier was used to detect voluntary movements and postural tremor based on features extracted from thalamic LFPs. Results: Cross-validation demonstrated that both voluntary movements and postural tremor can be decoded with an average sensitivity of 0.8 and false detection rate of 0.2. Oscillatory activities in the beta frequency bands (13-23 Hz) and the theta frequency bands (4-7 Hz) contributed most to the decoding of movements and postural tremor, respectively, though incorporating features in different frequency bands using a machine learning approach increased the accuracy of decoding. (C) 2019 The Author(s). Published by Elsevier Inc.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Tan, Huiling UNSPECIFIED orcid.org/0000-0001-8038-3029 UNSPECIFIED Debarros, Jean UNSPECIFIED UNSPECIFIED UNSPECIFIED He, Shenghong UNSPECIFIED UNSPECIFIED UNSPECIFIED Pogosyan, Alek UNSPECIFIED UNSPECIFIED UNSPECIFIED Aziz, Tipu Z. UNSPECIFIED UNSPECIFIED UNSPECIFIED Huang, Yongzhi UNSPECIFIED orcid.org/0000-0002-2503-1589 UNSPECIFIED Wang, Shouyan UNSPECIFIED UNSPECIFIED UNSPECIFIED Timmermann, Lars UNSPECIFIED UNSPECIFIED UNSPECIFIED Visser-Vandewalle, Veerle UNSPECIFIED UNSPECIFIED UNSPECIFIED Pedrosa, David J. UNSPECIFIED UNSPECIFIED UNSPECIFIED Green, Alexander L. UNSPECIFIED UNSPECIFIED UNSPECIFIED Brown, Peter UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-136746
DOI:	10.1016/j.brs.2019.02.011
Journal or Publication Title:	Brain Stimul.
Volume:	12
Number:	4
Page Range:	S. 858 - 868
Date:	2019
Publisher:	ELSEVIER SCIENCE INC
Place of Publication:	NEW YORK
ISSN:	1876-4754
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language DEEP BRAIN-STIMULATION; LOCAL-FIELD POTENTIALS; SUBTHALAMIC NUCLEUS; OSCILLATIONS; INVOLVEMENT; RECORDINGS; DISORDER; EFFICACY; TIME; DBS Multiple languages Clinical Neurology; Neurosciences Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/13674