An online movement and tremor identification algorithm for evaluation during deep brain stimulation
Loading...
Authors
Pambakian, Nicola
Coste, Jérôme
Lange, Ijsbrand de
Lemaire, Jean-Jacques
Author (Corporation)
Publication date
02.09.2022
Typ of student thesis
Course of study
Type
01A - Journal article
Editors
Editor (Corporation)
Supervisor
Parent work
Current Directions in Biomedical Engineering
Special issue
DOI of the original publication
Link
Series
Series number
Volume
8
Issue / Number
2
Pages / Duration
105-108
Patent number
Publisher / Publishing institution
De Gruyter
Place of publication / Event location
Edition
Version
Programming language
Assignee
Practice partner / Client
Abstract
INTRODUCTION: Deep brain stimulation (DBS) is widely used to alleviate symptoms of movement disorders. During intraoperative stimulation the influence of active or passive movements on the neuronal activity is often evaluated but the evaluation remains mostly subjective. The objective of this paper is to investigate the potential of a previously developed Weighted-frequency Fourier Linear combiner and Kalman filter-based recursive algorithm to identify tremor phases and types. METHODS: Ten accelerometer recordings from eight patients were acquired during DBS from which 186 phases were manually annotated into: rest, postural and kinetic phase without tremor, and rest, postural and kinetic phase with tremor. The method first estimates the instantaneous tremor frequency and then decomposes the motion signal into voluntary and tremorous parts. The tremorous part is used to quantify tremor and the voluntary part to differentiate rest, postural and kinetic phases. RESULTS: Instantaneous tremor frequency and amplitude are successfully tracked online. The overall accuracy for tremorous phases only is 89.1% and 76.3% when also non-tremorous phases are considered. Two main misclassification cases are identified and further discussed. CONCLUSION: The results demonstrate the potential of the developed algorithm as an online tremorous movement classifier. It would benefit from a more advanced tremor detector but nevertheless the obtained digital biomarkers offer an evidence-based analysis and could optimize the efficacy of DBS treatment.
Keywords
Tremor estimation, Weighted-frequency Fourier Linear combiner, Deep brain stimulation, Microelectrode recording, Digital biomarker
Subject (DDC)
600 - Technik, Medizin, angewandte Wissenschaften
Event
Exhibition start date
Exhibition end date
Conference start date
Conference end date
Date of the last check
ISBN
ISSN
2364-5504
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
Review
Peer review of the complete publication
Open access category
Gold
License
Citation
BOURGEOIS, Frédéric, Nicola PAMBAKIAN, Jérôme COSTE, Ijsbrand de LANGE, Jean-Jacques LEMAIRE und Simone HEMM-ODE, 2022. An online movement and tremor identification algorithm for evaluation during deep brain stimulation. Current Directions in Biomedical Engineering. 2 September 2022. Bd. 8, Nr. 2, S. 105–108. DOI 10.1515/cdbme-2022-1028. Verfügbar unter: https://doi.org/10.26041/fhnw-4611