Abstract
A new algorithm based on singular value decomposition (SVD) to remove cardiac contamination from trunk electromyography (EMG) is proposed. Its performance is compared to currently available algorithms at different signal-to-noise ratios (SNRs). The algorithm is applied on individual channels. An experimental calibration curve to adjust the number of SVD components to the SNR (0–20 dB) is proposed. A synthetic dataset is generated by the combination of electrocardiography (ECG) and EMG to establish a ground truth reference for validation. The performance is compared with state-of-the-art algorithms: gating, high-pass filtering, template subtraction (TS), and independent component analysis (ICA). Its applicability on real data is investigated in an illustrative diaphragm EMG of a patient with sleep apnea. The SVD-based algorithm outperforms existing methods in reconstructing trunk EMG. It is superior to the others in the time (relative mean squared error < 15%) and frequency (shift in mean frequency < 1 Hz) domains. Its feasibility is proven on diaphragm EMG, which shows a better agreement with the respiratory cycle (correlation coefficient = 0.81, p-value < 0.01) compared with TS and ICA. Its application on real data is promising to non-obtrusively estimate respiratory effort for sleep-related breathing disorders. The algorithm is not limited to the need for additional reference ECG, increasing its applicability in clinical practice.
Highlights
Trunk electromyography (EMG) is a non-invasive measure of the electrical activity of trunk muscles such as diaphragm, pectoralis, and intercostal muscles involved in respiration
The aim of the present study is to develop an singular value decomposition (SVD)-based algorithm able to improve the removal of ECG interference from trunk EMG, with respect to standard methods proposed in the field
To quantitatively assess the performance of the algorithms, we evaluated the error in the reconstructed signal both in the time and frequency domain analyzing two widely used EMG features, the relative mean squared error (MSEr) and the mean frequency difference (MFD)
Summary
Trunk electromyography (EMG) is a non-invasive measure of the electrical activity of trunk muscles such as diaphragm, pectoralis, and intercostal muscles involved in respiration. The use of trunk EMG to non–obtrusively estimate the respiratory effort is promising for the diagnosis of sleep-related breathing disorders [1,2]. The application of trunk EMG to respiratory monitoring is strongly hampered due to the contamination interference of cardiac electrical activity (ECG) that distorts both the amplitude and frequency content of the signal [3,4]. Typical values of the signal-to-noise ratio (SNR) on trunk EMG are around 10 dB to 20 dB, depending on the electrode position [3,5]. To monitor respiratory effort in clinical practice, obtrusive recording of esophageal pressure is considered more reliable than non-obtrusive trunk
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