Accessibility navigation


Resting tremor classification and detection in Parkinson's disease patients

Camara, C., Isasi, P., Warwick, K., Ruiz, V., Aziz, T., Stein, J. and Bakštein, E. (2015) Resting tremor classification and detection in Parkinson's disease patients. Biomedical Signal Processing and Control, 16. pp. 88-97. ISSN 1746-8094

Full text not archived in this repository.

To link to this item DOI: 10.1016/j.bspc.2014.09.006

Abstract/Summary

Parkinson is a neurodegenerative disease, in which tremor is the main symptom. This paper investigates the use of different classification methods to identify tremors experienced by Parkinsonian patients.Some previous research has focussed tremor analysis on external body signals (e.g., electromyography, accelerometer signals, etc.). Our advantage is that we have access to sub-cortical data, which facilitates the applicability of the obtained results into real medical devices since we are dealing with brain signals directly. Local field potentials (LFP) were recorded in the subthalamic nucleus of 7 Parkinsonian patients through the implanted electrodes of a deep brain stimulation (DBS) device prior to its internalization. Measured LFP signals were preprocessed by means of splinting, down sampling, filtering, normalization and rec-tification. Then, feature extraction was conducted through a multi-level decomposition via a wavelettrans form. Finally, artificial intelligence techniques were applied to feature selection, clustering of tremor types, and tremor detection.The key contribution of this paper is to present initial results which indicate, to a high degree of certainty, that there appear to be two distinct subgroups of patients within the group-1 of patients according to the Consensus Statement of the Movement Disorder Society on Tremor. Such results may well lead to different resultant treatments for the patients involved, depending on how their tremor has been classified. Moreover, we propose a new approach for demand driven stimulation, in which tremor detection is also based on the subtype of tremor the patient has. Applying this knowledge to the tremor detection problem, it can be concluded that the results improve when patient clustering is applied prior to detection.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science
ID Code:45604
Uncontrolled Keywords:Parkinson’s disease (PD) Tremo rLocal field potential (LFP) Deep brain stimulation (DBS) Discrete wavelet transform (DWT) Artificial neural network (ANN)
Publisher:Elsevier

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation