EMG Signal Filtering Based on Independent Component Analysis and Empirical Mode Decomposition for Estimation of Motor Activation Patterns

Claudio Tapia, Omar Daud, Javier Ruiz-del-Solar

Resultado de la investigación: Contribución a una revistaArtículo

5 Citas (Scopus)

Resumen

A method based on independent component analysis (ICA) and empirical mode decomposition (EMD) for processing electromyographic (EMG) signals is proposed. This method is used for determining the motor activation pattern of the lower extremities during walking. The method is evaluated by recording EMG signals from 11 healthy women. The EMG signals used for the analysis are recorded from 16 muscles of both lower limbs during walking. The method consists of preprocessing the EMG signals using principal component analysis, analyzing them using ICA, and finally filtering them using EMD. This approach allows the reconstruction of the original source signals by filtering the components that do not have a muscular origin. These data are then segmented and processed using the Hilbert transform in order to obtain a representation of a gait cycle from all records. The activation patterns obtained with this method are compared to the ones obtained using a conventional method, based on low-pass filtering, and a method based only on EMD filtering. According to the results, the proposed method obtains a better sequence of motor activation during walking. The proposed method is validated by a comparison of the results with kinematic behavior (expressed as the angular movement of the hip, knee, and ankle of each participant) and statistical significance analysis.

Idioma originalInglés
Páginas (desde-hasta)140-155
Número de páginas16
PublicaciónJournal of Medical and Biological Engineering
Volumen37
N.º1
DOI
EstadoPublicada - 1 feb 2017

Áreas temáticas de ASJC Scopus

  • Medicina (todo)
  • Ingeniería biomédica

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