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.

Objective: Surface electromyography (sEMG) is a noninvasive option for monitoring respiratory effort in ventilated patients. However, respiratory sEMG signals are affected by crosstalk and cardiac activity. This work addresses the blind source separation (BSS) of inspiratory and expiratory electrical activity in single- or two-channel recordings. The main contribution of the presented methodology is its applicability to the addressed muscles and the number of available channels. Approach: We propose a two-step procedure consisting of a single-channel cardiac artifact removal algorithm, followed by a single- or multi-channel BSS stage. First, cardiac components are removed in the wavelet domain. Subsequently, a nonnegative matrix factorization (NMF) algorithm is applied to the envelopes of the resulting wavelet bands. The NMF is initialized based on simultaneous standard pneumatic measurements of the ventilated patient. Main results: The proposed estimation scheme is applied to twelve clinical datasets and simulated sEMG signals of the respiratory system. The results on the clinical datasets are validated based on expert annotations using invasive pneumatic measurements. In the simulation, three measures evaluate the separation success: The distortion and the correlation to the known ground truth and the inspiratory-to-expiratory signal power ratio. We find an improvement across all SNRs, recruitment patterns, and channel configurations. Moreover, our results indicate that the initialization strategy replaces the manual matching of sources after the BSS. Significance: The proposed separation algorithm facilitates the interpretation of respiratory sEMG signals. In crosstalk affected measurements, the developed method may help clinicians distinguish between inspiratory effort and other muscle activities using only noninvasive measurements.