Key result
The proposed artificial neural network-wavelet method successfully removed ECG artifacts from surface EMG signals, achieving a signal to noise ratio of 15.6015 and a relative error of 0.0139.
Why the study?
Does a combination of artificial neural network and wavelet transform improve the removal of ECG artifacts from surface EMG signals compared to conventional methods?
Does a combination of artificial neural network and wavelet transform improve the removal of ECG artifacts from surface EMG signals compared to conventional methods?
A combined artificial neural network and wavelet transform approach effectively removes ECG artifacts from surface EMG signals, yielding high signal-to-noise ratio and low relative error.
The electrocardiogram signal which represents the electrical activity of the heart provides interference in the recording of the electromyogram signal, when the electromyogram signal is recorded from muscles close to the heart. Therefore, due to impurities, electromyogram signals recorded from this area cannot be used. In this paper, a new method was developed using a combination of artificial neural network and wavelet transform approaches, to eliminate the electrocardiogram artifact from electromyogram signals and improve results. For this purpose, contaminated signal is initially cleaned using the neural network. With this process, a large amount of noise can be removed. However, low-frequency noise components remain in the signal that can be removed using wavelet. Finally, the result of the proposed method is compared with other methods that were used in different papers to remove electrocardiogram from electromyogram. In this paper in order to compare methods, qualitative and quantitative criteria such as signal to noise ratio, relative error, power spectrum density and coherence have been investigated for evaluation and comparison. The results of signal to noise ratio and relative error are equal to 15.6015 and 0.0139, respectively.
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Abbaspour et al. (2014) studied Healthy (n=5). Artificial Neural Network combined with Wavelet Transform (ANN-wavelet) vs. Other noise removal methods (HPF, wavelet transform, adaptive filter, ANN, subtraction) was evaluated on Signal to noise ratio (SNR) and relative error (RE). The proposed artificial neural network-wavelet method successfully removed ECG artifacts from surface EMG signals, achieving a signal to noise ratio of 15.6015 and a relative error of 0.0139.
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