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Title

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
Tremor
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
URIhttp://hdl.handle.net/10261/74238
DOIhttp://dx.doi.org/10.1109/NER.2011.5910493
E-ISSN978-1-4244-4141-9
Appears in Collections:(CAR) Comunicaciones congresos
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