Abstract
This work investigates elimination methods for cardiogenic artifacts in respiratory surface electromyographic (sEMG) signals and compares their performance with respect to subsequent fatigue detection with different fatigue algorithms. The analysis is based on artificially constructed test signals featuring a clearly defined expected fatigue level. Test signals are additively constructed with different proportions from sEMG and electrocardiographic (ECG) signals. Cardiogenic artifacts are eliminated by high-pass filtering (HP), template subtraction (TS), a newly introduced two-step approach (TSWD) consisting of template subtraction and a wavelet-based damping step and a pure wavelet-based damping (DSO). Each method is additionally combined with the exclusion of QRS segments (gating). Fatigue is subsequently quantified with mean frequency (MNF), spectral moments ratio of order five (SMR5) and fuzzy approximate entropy (fApEn). Different combinations of artifact elimination methods and fatigue detection algorithms are tested with respect to their ability to deliver invariant results despite increasing ECG contamination. Both DSO and TSWD artifact elimination methods displayed promising results regarding the intermediate, “cleaned” EMG signal. However, only the TSWD method enabled superior results in the subsequent fatigue detection across different levels of artifact contamination and evaluation criteria. SMR5 could be determined as the best fatigue detection algorithm. This study proposes a signal processing chain to determine neuromuscular fatigue despite the presence of cardiogenic artifacts. The results furthermore underline the importance of selecting a combination of algorithms that play well together to remove cardiogenic artifacts and to detect fatigue. This investigation provides guidance for clinical studies to select optimal signal processing to detect fatigue from respiratory sEMG signals.
Highlights
The over-exaggerated use of skeletal muscles leads to fatigue recognizable in the corresponding surface electromyographic signals [1,2,3]
The following section starts with the presentation of results regarding the quality of the artificial test signals as well as the influence of EMG level and ECG channel
As this evaluation aimed at the ECG component only, it was performed based on ECGI (t) and ECGA (t) instead of the artificial test signals ATSΛ,η,ch (t), including EMG
Summary
The over-exaggerated use of skeletal muscles leads to fatigue recognizable in the corresponding surface electromyographic (sEMG) signals [1,2,3]. In the case of mechanical ventilation of the critically ill patient, fatigued respiratory muscles are related to a pathway leading to respiratory failure [7]. Respiratory failure is a clinically relevant issue in connection with weaning a patient from mechanical ventilation, especially if this process fails. Weaning failure is associated with an imbalance of neuromuscular capacity and ventilatory needs [8]. This failure is in many cases of a multi-factorial nature [8], it is often accompanied with respiratory muscle fatigue
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