Resting tremor classification and detection in Parkinson's disease patients

Cámara Nuñez, Carmen; Isabi, Pedro; Warwic, Kevin; Ruiz, Virginie; Aziz, Tipu; Stein, John y Bakstein, Eduard (2015). Resting tremor classification and detection in Parkinson's disease patients. "Biomedical Signal Processing and Control", v. 16 ; pp. 88-97. ISSN 1746-8094. https://doi.org/10.1016/j.bspc.2014.09.006.

Descripción

Título: Resting tremor classification and detection in Parkinson's disease patients
Autor/es:
  • Cámara Nuñez, Carmen
  • Isabi, Pedro
  • Warwic, Kevin
  • Ruiz, Virginie
  • Aziz, Tipu
  • Stein, John
  • Bakstein, Eduard
Tipo de Documento: Artículo
Título de Revista/Publicación: Biomedical Signal Processing and Control
Fecha: Febrero 2015
Volumen: 16
Materias:
Palabras Clave Informales: Parkinson's disease (PD); Tremor; Local field potential (LFP); Deep brain stimulation (DBS); Discrete wavelet transform (DWT); Artificial neural network (ANN)
Escuela: Centro de Tecnología Biomédica (CTB) (UPM)
Departamento: Otro
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

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 rectification. Then, feature extraction was conducted through a multi-level decomposition via a wavelet transform. 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.

Más información

ID de Registro: 43635
Identificador DC: http://oa.upm.es/43635/
Identificador OAI: oai:oa.upm.es:43635
Identificador DOI: 10.1016/j.bspc.2014.09.006
URL Oficial: http://www.sciencedirect.com/science/article/pii/S1746809414001414
Depositado por: Memoria Investigacion
Depositado el: 25 Abr 2017 17:59
Ultima Modificación: 25 Abr 2017 17:59
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