Removing Cardiac Artifacts From Single-Channel Respiratory Electromyograms

Abstract

Electromyographic (EMG) measurements of the respiratory muscles provide a convenient and noninvasive way to assess respiratory muscle function and detect patient activity during assisted mechanical ventilation. However, surface EMG measurements of the diaphragm and intercostal muscles are substantially contaminated by cardiac activity due to the vicinity of the cardiac muscles. Many algorithmic solutions to this problem have been proposed, yet a conclusive performance comparison of the most promising candidates currently is missing. The objective of this work is to provide a quantitative performance comparison of six previously proposed cardiac artifact removal algorithms operating on single-channel EMG measurements, and two newly proposed, improved versions of these algorithms. Algorithmic performance is evaluated quantitatively based on four different measures of separation success, using both synthetic validation signals and electromyographic measurements of the respiratory muscles in eight subjects. The compared algorithms are two versions of the empirical template subtraction algorithm, two model-based Bayesian filtering procedures, a wavelet denoising approach, an empirical mode decomposition (EMD) based approach, and classical high-pass filtering. Different algorithms perform well with respect to different performance measures. Template subtraction algorithms yield the best results if the characteristics of the raw signal are of interest, while filtering algorithms like simple high-pass filtering, wavelet denoising, and EMD-based denoising show superior performance for calculating a cleaned envelope signal. No algorithm completely removes the cardiac interference, but the characteristic errors introduced by the considered algorithms differ. Hence, the choice of the algorithm to use should be made depending on the target application. Finally, we also demonstrate that our empirical SNR measure, which can be calculated without knowledge of the true, undisturbed signals, correlates strongly with the exact reconstruction error. Thus, it represents a reliable indicator for algorithm performance on real measurement data.

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

“…Artificially constructed test signals were also used for the evaluation of algorithms eliminating cardiogenic artifacts from sEMG signals by Deng et al [ 33 ] and Petersen et al [ 15 ]. Our approach differs in two key aspects.…”

“…This way, we could assure a clearly defined expected result, as the underlying pure sEMG signal from static contractions offered a linear onset in fatigue measures. Our approach to modulate an sEMG signal from a static contraction can be seen as a compromise between approaches used by Deng et al [ 33 ] and Petersen et al [ 15 ]. The first group of authors used differently filtered versions of white Gaussian noise for inspiratory and expiratory segments.…”

“…The squared error of the ECG attenuated signal with removal method “cr” is defined as: . This corresponds to the definition of the error for synthetic signals by Petersen et al [ 15 ]. Subpanels ( A , B ) show the pure EMG component and the ECG “cleaned” signal , respectively.…”

“…The mean removal error across all nine subjects is displayed in ( C ) for different ECG removal logarithms and EMG levels . The influence of ECG removal variants on a subsequently calculated envelope signal is quantified by an error , as known from Petersen et al [ 15 ]. The mean average value MAV with a centralized sliding window (size 128) is calculated for both the pure EMG component and the cleaned signal.…”

“…This template is used in an adaptive noise cancellation scheme by subtracting it from the contaminated signal repetitively aligned with heartbeats. A study comparing different elimination methods for cardiogenic artifacts in respiratory sEMG was recently published by Petersen et al [ 15 ].…”