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.
Highlights
Monitoring the activity of the respiratory muscles is of critical importance in respiratory care [1]–[5], and can be achieved continuously and noninvasively using surface electromyography (EMG) [6]–[8]
Especially at relatively high signal-to-noise ratio (SNR), the two template subtraction algorithms and the EKS2 yield the best results with respect to the raw EMG signal
Removing cardiac interference from single-channel surface EMG measurements of the respiratory muscles is a challenging task for which many different algorithms have been proposed
Summary
Monitoring the activity of the respiratory muscles is of critical importance in respiratory care [1]–[5], and can be achieved continuously and noninvasively using surface electromyography (EMG) [6]–[8]. The interpretation of EMG measurements of the respiratory muscles is hindered by interference due to cardiac muscle activity. The close vicinity of the recording electrodes to the heart and the strength of the cardiac muscles make cardiac contaminants typically surpass the sought muscle signals by orders of magnitude. Removal of cardiac artifacts from the measured signals proves crucial for the diagnostic interpretation of respiratory EMG measurements and has engaged researchers for many decades [9], [10]. Note that the estimation of an ECG-derived respiration (EDR) signal is a closely related task that has been considered by many researchers (for a comprehensive review refer to, e.g., Charlton et al [11]). EDR estimation is a significantly easier task than recovering the original respiratory EMG signal, as in the EDR scenario, the exact shape of the EMG signal is irrelevant
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