A Combination Method for Electrocardiogram Rejection from Surface Electromyogram

Abstract

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.

Removing Cardiac Artifacts From Single-Channel Respiratory Electromyograms

“…The described wavelet-based approach yields a denoised version of the ECG component, which is then subtracted from the raw signal to recover the EMG component. 4) As opposed to Abbaspour and Fallah [26] and Abbaspour et al [27], who used an adaptive filtering method as an additional preprocessing step, we applied our wavelet denoising method to the raw EMG signal.…”

“…Previously presented comparisons and validations are based only on numerical simulations [31], a single subject [29], [32], [33], or synthetically generated signals, composed by combining clean EMG measurements with clean ECG measurements [21], [26], [32], [33]. To the author's knowledge, only Slim and Raoof [25] have analyzed real respiratory EMG measurements from multiple subjects, and they only analyzed a single measure of separation success (which is not sufficient, as will be discussed in section V) and only short segments of measurements.…”

Removal of ECG Artifacts Affects Respiratory Muscle Fatigue Detection—A Simulation Study

“…A two-step approach to further reduce cardiogenic artifacts that could not be eliminated by one method is also known from Abbaspour and Fallah [ 32 ]. Our approach differs in two key aspects besides the fact that they used an artificial neural network (ANN) to obtain an estimation of ECG for subtraction in the first step instead of ordinary template subtraction.…”

“…The first aspect concerns the elimination of artifacts. Abbaspour and Fallah [ 32 ] set the wavelet coefficient to zero, if it exceeds a threshold. This corresponds to wavelet denoising with hard thresholding.…”

“…On the other hand, the hurdles for the removal of an artifact are higher if it is located in segments where cardiogenic artifacts are very unlikely. In contrast, Abbaspour and Fallah [ 32 ] do not adopt the threshold depending on the heart phase.…”