An online movement and tremor identification algorithm for evaluation during deep brain stimulation
Lade...
Autor:innen
Pambakian, Nicola
Coste, Jérôme
Lange, Ijsbrand de
Lemaire, Jean-Jacques
Autor:in (Körperschaft)
Publikationsdatum
02.09.2022
Typ der Arbeit
Studiengang
Typ
01A - Beitrag in wissenschaftlicher Zeitschrift
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
Current Directions in Biomedical Engineering
Themenheft
DOI der Originalpublikation
Link
Reihe / Serie
Reihennummer
Jahrgang / Band
8
Ausgabe / Nummer
2
Seiten / Dauer
105-108
Patentnummer
Verlag / Herausgebende Institution
De Gruyter
Verlagsort / Veranstaltungsort
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
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.
Schlagwörter
Tremor estimation, Weighted-frequency Fourier Linear combiner, Deep brain stimulation, Microelectrode recording, Digital biomarker
Fachgebiet (DDC)
600 - Technik, Medizin, angewandte Wissenschaften
Veranstaltung
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
Enddatum der Konferenz
Datum der letzten Prüfung
ISBN
ISSN
2364-5504
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Publikationsstatus
Veröffentlicht
Begutachtung
Peer-Review der ganzen Publikation
Open Access-Status
Gold
Lizenz
Zitation
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