Miljković Nadica, Popović Nenad, Djordjević Olivera, Konstantinović Ljubica, Šekara Tomislav B
University of Belgrade, School of Electrical Engineering, Bulevar kralja Aleksandra 73, 11000 Belgrade, Serbia.
Rehabilitation Clinic "Dr Miroslav Zotović", Sokobanjska 13, 11000 Belgrade, Serbia; University of Belgrade, School of Medicine, Dr Subotića 8, 11000 Belgrade, Serbia.
Comput Methods Programs Biomed. 2017 Mar;140:259-264. doi: 10.1016/j.cmpb.2016.12.017. Epub 2017 Jan 4.
New aspects for automatic electrocardiography artifact removal from surface electromyography signals by application of fractional order calculus in combination with linear and nonlinear moving window filters are explored. Surface electromyography recordings of skeletal trunk muscles are commonly contaminated with spike shaped artifacts. This artifact originates from electrical heart activity, recorded by electrocardiography, commonly present in the surface electromyography signals recorded in heart proximity. For appropriate assessment of neuromuscular changes by means of surface electromyography, application of a proper filtering technique of electrocardiography artifact is crucial.
A novel method for automatic artifact cancellation in surface electromyography signals by applying fractional order calculus and nonlinear median filter is introduced. The proposed method is compared with the linear moving average filter, with and without prior application of fractional order calculus. 3D graphs for assessment of window lengths of the filters, crest factors, root mean square differences, and fractional calculus orders (called WFC and WRC graphs) have been introduced. For an appropriate quantitative filtering evaluation, the synthetic electrocardiography signal and analogous semi-synthetic dataset have been generated. The examples of noise removal in 10 able-bodied subjects and in one patient with muscle dystrophy are presented for qualitative analysis.
The crest factors, correlation coefficients, and root mean square differences of the recorded and semi-synthetic electromyography datasets showed that the most successful method was the median filter in combination with fractional order calculus of the order 0.9. Statistically more significant (p < 0.001) ECG peak reduction was obtained by the median filter application compared to the moving average filter in the cases of low level amplitude of muscle contraction compared to ECG spikes.
The presented results suggest that the novel method combining a median filter and fractional order calculus can be used for automatic filtering of electrocardiography artifacts in the surface electromyography signal envelopes recorded in trunk muscles.
探索通过将分数阶微积分与线性和非线性移动窗口滤波器相结合，从表面肌电图信号中自动去除心电图伪迹的新方法。躯干骨骼肌的表面肌电图记录通常会受到尖峰状伪迹的干扰。这种伪迹源于心电图记录的心脏电活动，通常存在于靠近心脏部位记录的表面肌电图信号中。为了通过表面肌电图适当地评估神经肌肉变化，应用适当的心电图伪迹滤波技术至关重要。
介绍一种通过应用分数阶微积分和非线性中值滤波器在表面肌电图信号中自动消除伪迹的新方法。将所提出的方法与线性移动平均滤波器进行比较，比较有无分数阶微积分的先验应用情况。引入了用于评估滤波器窗口长度、波峰因数、均方根差和分数阶微积分阶数的三维图（称为WFC和WRC图）。为了进行适当的定量滤波评估，生成了合成心电图信号和类似的半合成数据集。给出了10名健康受试者和1名肌肉营养不良患者的噪声去除示例，用于定性分析。
记录的和半合成肌电图数据集的波峰因数、相关系数和均方根差表明，最成功的方法是中值滤波器与0.9阶分数阶微积分相结合。在肌肉收缩幅度低于心电图尖峰的情况下，与移动平均滤波器相比，应用中值滤波器在统计学上获得了更显著的（p < 0.001）心电图峰值降低。
所呈现的结果表明，结合中值滤波器和分数阶微积分的新方法可用于自动滤波记录在躯干肌肉表面肌电图信号包络中的心电图伪迹。
