An automated ECG-artifact removal method for trunk muscle surface EMG recordings.
Med Eng Phys
; 32(8): 840-8, 2010 Oct.
Article
en En
| MEDLINE
| ID: mdl-20561810
This study aimed at developing a method for automated electrocardiography (ECG) artifact detection and removal from trunk electromyography signals. Independent Component Analysis (ICA) method was applied to the simulated data set of ECG-corrupted surface electromyography (SEMG) signals. Independent Components (ICs) correspond to ECG artifact were then identified by an automated detection algorithm and subsequently removed. The detection performance of the algorithm was compared to that by visual inspection, while the artifact elimination performance was compared with Butterworth high pass filter at 30 Hz cutoff (BW HPF 30). The automated ECG-artifact detection algorithm successfully recognized the ECG source components in all data sets with a sensitivity of 100% and specificity of 99%. Better performance indicated by a significantly higher correlation coefficient (p<0.001) with the original EMG recordings was found in the SEMG data cleaned by the ICA-based method, than that by BW HPF 30. The automated ECG-artifact removal method for trunk SEMG recordings proposed in this study was demonstrated to produce a very good detection rate and preserved essential EMG components while keeping its distortion to minimum. The automatic nature of our method has solved the problem of visual inspection by standard ICA methods and brings great clinical benefits.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Procesamiento de Señales Asistido por Computador
/
Artefactos
/
Electrocardiografía
/
Electromiografía
/
Músculos
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Med Eng Phys
Asunto de la revista:
BIOFISICA
/
ENGENHARIA BIOMEDICA
Año:
2010
Tipo del documento:
Article
País de afiliación:
Hong Kong
Pais de publicación:
Reino Unido