English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/74238
logo share SHARE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE
Exportar a otros formatos:


Analysis of kinematic data in pathological tremor with the Hilbert–Huang transform

AuthorsGallego, Juan Álvaro CSIC ORCID CVN; Rocón, Eduardo CSIC ORCID; Koutsou, Aikaterini CSIC ORCID CVN; Pons Rovira, José Luis CSIC ORCID
KeywordsHilbert-Huang transform
Empirical mode decomposition
Ensemble empirical mode decomposition
Parkinson's disease
Essential tremor
Movement analisysis
Blind source separation
Issue Date2011
PublisherInstitute of Electrical and Electronics Engineers
CitationProceedings of the 5th International IEEE EMBS Conference on Neural Engineering
AbstractThis paper presents analysis of kinematic data of tremor patients while performing different tasks with Ensemble Empirical Mode Decomposition (EEMD), a novel noise–assisted data analysis method. EEMD automatically separates raw kinematic data into three components: 1) noise from various sources, 2) tremulous movement, and 3) voluntary movement. Comparison of this technique with other decomposition meth- ods such as recursive forth and back filters or Empirical Mode Decomposition (EMD) shows a better performance; EEMD separation of tremor diminishes EMD error in a 45.2 % (mean error 0.041 ± 0.036 rad/s). Moreover, postprocessing of EEMD separated tremor allows the calculation of the Hilbert spectrum, a high resolution time–energy–frequency distribution that improves analysis of tremors
Publisher version (URL)http://dx.doi.org/10.1109/NER.2011.5910493
Appears in Collections:(CAR) Comunicaciones congresos
Files in This Item:
File Description SizeFormat 
gallego_NER2011_final.pdf918,56 kBAdobe PDFThumbnail
Show full item record
Review this work

WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.