|کد مقاله||کد نشریه||سال انتشار||مقاله انگلیسی||ترجمه فارسی||نسخه تمام متن|
|4973461||1451641||2018||10 صفحه PDF||سفارش دهید||دانلود کنید|
- An efficient method is presented to remove ECG from EMG with limited deterioration.
- The ECG pulses are first localized and then remove only where they have been detected.
- A combination of ICA and DWT is first used to extract ECG information.
- Then, the pulses positions are detected with an original algorithm based on FFT which takes advantage of the quasi-periodic nature of the ECG.
- The proposed method accurately detects pulses positions and efficiently removes the ECG from EMG signals even when both signals are strongly overlapped.
The interpretations of the surface electromyography (sEMG) signals from the trunk region are strongly distorted by the heart activity (ECG), especially in case of low-amplitude EMG responses analyses. Many methods have been investigated to resolve this nontrivial problem, by using advanced data processing on the overall sEMG recorded signal. However, if they reduce ECG artifacts, those cancellation methods also deteriorate noiseless parts of the signal. This work proposes an original ECG cancellation method designed to limit the deterioration of sEMG information. To do that, the proposed techniques does not directly attempt to remove the ECG, but is based on two main steps: the localization of ECG and the cancellation of ECG but only where heart pulses have been detected. The phase of localization efficiently extracts the ECG contribution by combining the discrete wavelet transforms (DWT) and the method of independent component analysis (ICA). And finally, this phase takes advantage of quasi-periodic properties of ECG signals to accurately detect pulses localization with an original algorithm based on the fast Fourier transform (FFT). Intensive simulations were achieved in terms of relative errors, coherence and accuracy for different levels of ECG interference. And the correlation coefficients computed from the paraspinal muscles EMG signals of 12 healthy participants were also used to evaluate the developed method. The results from simulation and real data demonstrate that the proposed method accurately detects pulses positions and efficiently removes the ECG from EMG signals, even when both signals are strongly overlapped, and greatly limits the deterioration of the EMG.
Journal: Biomedical Signal Processing and Control - Volume 39, January 2018, Pages 1-10