A method for the synchronization of inertial sensor signals and local field potentials from deep brain stimulation systems.

Deep Brain Stimulation Local Field Potential Parkinson’s disease Synchronization Wearable sensors

Journal

Biomedical physics & engineering express
ISSN: 2057-1976
Titre abrégé: Biomed Phys Eng Express
Pays: England
ID NLM: 101675002

Informations de publication

Date de publication:
03 Jul 2024
Historique:
medline: 4 7 2024
pubmed: 4 7 2024
entrez: 3 7 2024
Statut: aheadofprint

Résumé


Recent innovative neurostimulators allow recording local field potentials (LFPs) while performing motor tasks monitored by wearable sensors. Inertial sensors can provide quantitative measures of motor impairment in people with subthalamic nucleus deep brain stimulation. To the best of our knowledge, there is no validated method to synchronize inertial sensors and neurostimulators without an additional device. This study aims to define a new synchronization method to analyze disease-related brain activity patterns during specific motor tasks and evaluate how LFPs are affected by stimulation and medication. 
Approach:
Twelve male subjects treated with subthalamic nucleus deep brain stimulation were recruited to perform motor tasks in four different medication and stimulation conditions. In each condition, a synchronization protocol was performed consisting of taps on the implanted device, which produces artifacts in the LFPs that an inertial sensor can simultaneously record. 
Main results:
In 64% of the recruited subjects, induced artifacts were detected at least once. Among those subjects, 83% of the recordings could be correctly synchronized offline. The remaining recordings were synchronized by video analysis.
Significance: 
The proposed synchronization method does not require an external system and can be easily integrated into clinical practice. The procedure is simple and can be carried out in a short time. A proper and simple synchronization will also be useful to analyze subthalamic neural activity in the presence of specific events (e.g., freezing of gait events) to identify predictive biomarkers.&#xD.

Identifiants

pubmed: 38959873
doi: 10.1088/2057-1976/ad5e83
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Creative Commons Attribution license.

Auteurs

Ilaria D'Ascanio (I)

Department of Electrical, Electronic, and Information Engineering, University of Bologna, Viale Risorgimento 2, Bologna, Emilia-Romagna, 40136, ITALY.

Giulia Giannini (G)

IRCCS Istituto Delle Scienze Neurologiche di Bologna, Via Altura 3, Bologna, Italy, 40139, ITALY.

Luca Baldelli (L)

IRCCS Istituto Delle Scienze Neurologiche di Bologna, Via Altura 3, Bologna, Italy, 40139, ITALY.

Ilaria Cani (I)

IRCCS Istituto Delle Scienze Neurologiche di Bologna, Via Altura 3, Bologna, 40139, ITALY.

Alice Giannoni (A)

Department of Electrical, Electronic, and Information Engineering, University of Bologna, Viale Risorgimento 2, Bologna, Emilia-Romagna, 40136, ITALY.

Gaetano Leogrande (G)

Medtronic EMEA Corporate Technology & Innovation, Endepolsdomein 5, Maastricht, 6229, NETHERLANDS.

Giovanna Lopane (G)

Unit of Rehabilitation Medicine, IRCCS Istituto Delle Scienze Neurologiche di Bologna, Via Altura 3, Bologna, Emilia-Romagna, 40139, ITALY.

Giovanna Calandra Buonaura (G)

Department of Biomedical and NeuroMotor Sciences (DiBiNeM), University of Bologna, Via Ugo Foscolo 7, Bologna, Italy, 40123, ITALY.

Pietro Cortelli (P)

Universita degli Studi di Bologna Dipartimento di Scienze Biomediche e NeuroMotorie, Via Ugo Foscolo 7, Bologna, 40123, ITALY.

Lorenzo Chiari (L)

Department of Electrical, Electronic, and Information Engineering, University of Bologna, Viale Risorgimento 2, Bologna, Emilia-Romagna, 40136, ITALY.

Luca Palmerini (L)

Department of Electrical, Electronic, and Information Engineering, University of Bologna, Viale Risorgimento 2, Bologna, Emilia-Romagna, 40126, ITALY.

Classifications MeSH