Vol. 3 Issue 3
Year:2015
Issue:Jul-Sep
Title:A Combined Noise Filtration Approach for EMG Signals Using Classical Filters with Independent Component Analysis (ICA)
Author Name:Pradeep Kumar Jaisal, R. N. Patel
Synopsis:
Noise can limit the extraction of some basic and vital peculiarities from biomedical signals and thus makes it impossible to perform exact analysis of these signals. EMG (Electromyography) signals is one such case, which can be affected by number of factors. For example, power line noises, noises caused by electrical and electronic equipments, inherent semiconductor devices noises, etc. Electromyography (EMG) signals can be used for clinical/biomedical application and modern human computer interaction. EMG signals acquire noise while traveling through tissue, inherent noise in electronics equipment, ambient noise, and so forth. This paper presents an independent component analysis approach for removing noise from raw EMG signals. As the base of the presented systems is independent component analysis, but the technique also uses a multistep approach of filtering and combining the signals to recover the lost components also. The simulation results show that the proposed algorithm removes the noise without compromising the useful information of signal.
Year:2015
Issue:Jul-Sep
Title:A Combined Noise Filtration Approach for EMG Signals Using Classical Filters with Independent Component Analysis (ICA)
Author Name:Pradeep Kumar Jaisal, R. N. Patel
Synopsis:
Noise can limit the extraction of some basic and vital peculiarities from biomedical signals and thus makes it impossible to perform exact analysis of these signals. EMG (Electromyography) signals is one such case, which can be affected by number of factors. For example, power line noises, noises caused by electrical and electronic equipments, inherent semiconductor devices noises, etc. Electromyography (EMG) signals can be used for clinical/biomedical application and modern human computer interaction. EMG signals acquire noise while traveling through tissue, inherent noise in electronics equipment, ambient noise, and so forth. This paper presents an independent component analysis approach for removing noise from raw EMG signals. As the base of the presented systems is independent component analysis, but the technique also uses a multistep approach of filtering and combining the signals to recover the lost components also. The simulation results show that the proposed algorithm removes the noise without compromising the useful information of signal.
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