# A movement and tremor identification algorithm for evaluations during deep brain stimulation

Frédéric Bourgeois, Nicola Pambakian, Jérôme Coste, Ijsbrand de Lange, Jean-Jacques Lemaire, Erik Schkommodau, Simone Hemm

► To cite this version:

Frédéric Bourgeois, Nicola Pambakian, Jérôme Coste, Ijsbrand de Lange, Jean-Jacques Lemaire, et al.. A movement and tremor identification algorithm for evaluations during deep brain stimulation. Joint Annual Conference of the Austrian, German and Swiss Societies for Biomedical Engineering | BMT 2022, Austrian, German and Swiss Societies for Biomedical Engineering, Sep 2022, Innsbruck, Austria. pp.137. hal-03694034

HAL Id: hal-03694034

https://hal.science/hal-03694034

Submitted on 13 Jun 2022

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# A movement and tremor identification algorithm for evaluations during deep brain stimulation

Frédéric Bourgeois*¹, Nicola Pambakian², Jérôme Coste³, IJsbrand de Lange², Jean-Jacques Lemaire³, Erik Schkommodau¹ and Simone Hemm¹

**Corresponding author:** * frederic.bourgeois@fhnw.ch

1. Institute for Medical Engineering and Medical Informatics, School of Life Sciences, University of Applied Sciences and Arts Northwestern Switzerland, Muttenz, Switzerland, frederic.bourgeois@fhnw.ch

2. STIL B.V., Delft, Netherlands

3. Université Clermont Auvergne, Clermont Auvergne INP, CHU Clermont-Ferrand, CNRS, Institut Pascal, F-63000 Clermont-Ferrand, France

**Abstract:** Deep brain stimulation 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 algorithm to identify tremor types and to isolate the tremorous part. The method is applied to ten intraoperatively acquired accelerometer recordings from eight patients from which 186 phases were manually annotated into: rest, postural and kinetic phase without tremor, and rest, postural and kinetic phase with tremor. 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. The results demonstrate the potential of the developed algorithm for the use as an online tremorous movement classifier based on the acquired digital biomarkers.

**Keywords:** Tremor estimation, Deep Brain Stimulation, Microelectrode Recording, Weighted-frequency Fourier Linear combiner, digital biomarker.

**Submission Id=123 accepted as Oral Presentation (decision 2022-06-07)**

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