Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands.
School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China.
Sensors (Basel). 2021 Jan 15;21(2):573. doi: 10.3390/s21020573.
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, -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.
提出了一种基于奇异值分解（SVD）的新算法，用于从躯干肌电图（EMG）中去除心脏污染。将其性能与不同信噪比（SNR）下现有的算法进行了比较。该算法应用于单个通道。提出了一种实验校准曲线，用于根据 SNR（0-20dB）调整 SVD 分量的数量。通过心电图（ECG）和 EMG 的组合生成合成数据集，以建立用于验证的真实参考。将其性能与最先进的算法（门控、高通滤波、模板减法（TS）和独立成分分析（ICA））进行了比较。在睡眠呼吸暂停患者的膈肌 EMG 的说明性实例中研究了其在真实数据上的适用性。基于 SVD 的算法在重建躯干 EMG 方面优于现有方法。在时间（相对均方误差 < 15%）和频率（平均频率偏移 < 1Hz）方面优于其他方法。在膈肌 EMG 上证明了其可行性，与 TS 和 ICA 相比，它与呼吸周期具有更好的一致性（相关系数=0.81，p 值<0.01）。在真实数据上的应用有望非侵入性地估计与睡眠相关的呼吸障碍的呼吸努力。该算法不限于对额外参考 ECG 的需求，增加了其在临床实践中的适用性。
