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Título: | Analysis of kinematic data in pathological tremor with the Hilbert–Huang transform |
Autor: | Gallego, Juan Álvaro CSIC ORCID CVN; Rocón, Eduardo CSIC ORCID; Koutsou, Aikaterini CSIC ORCID CVN; Pons Rovira, José Luis CSIC ORCID | Palabras clave: | Hilbert-Huang transform Empirical mode decomposition Tremor Ensemble empirical mode decomposition Parkinson's disease Essential tremor Movement analisysis Blind source separation |
Fecha de publicación: | 2011 | Editor: | Institute of Electrical and Electronics Engineers | Citación: | Proceedings of the 5th International IEEE EMBS Conference on Neural Engineering | Resumen: | This 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 | Versión del editor: | http://dx.doi.org/10.1109/NER.2011.5910493 | URI: | http://hdl.handle.net/10261/74238 | DOI: | 10.1109/NER.2011.5910493 | E-ISSN: | 978-1-4244-4141-9 |
Aparece en las colecciones: | (CAR) Comunicaciones congresos |
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gallego_NER2011_final.pdf | 918,56 kB | Adobe PDF | Visualizar/Abrir |
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