# Processing of EMG Signals with High Impact of Power Line and Cardiac Interferences
## 3 Results and Discussion  
In order to demonstrate the effectiveness of the proposed signal processing, several measurements of the abdominal EMG signal were performed. Particular attention was paid to signal processing with a relatively high influence of the 50 Hz power line and cardiac disturbances. The filtering proposed in this article has been applied to reduce the high impact of interference--Figure 14.  
Figure 14: Examples of filters’ characteristics for EMG signals, (**a**) magnitude of 3 rd order BP filter (\(f_{lc}\) = 30 Hz, \(f_{hc}\) = 400 Hz) and 40 th order comb filter for \(\Delta f\) = \(\pm\)0.5 Hz, (**b**) magnitude of 3 rd order HP filter (\(f_{c}\) = 30 Hz) and 40 th order comb filter for \(\Delta f\) = \(\pm\)0.5 Hz.  
Figure 13: Scheme of RMS signal recovery.  
It should be emphasized that all the signals presented in the article were acquired by the new system developed during this research. This system is used not only to measure biopotentials, but also to stimulate muscle contraction. The proposed signal processing allows the doctor to choose the appropriate filtering depending on the type of muscles diagnosed. The system offers BP or HP preprocessing to eliminate low-frequency drift. All processing is carried out in the form of the pipeline architecture. Just after pre-processing, power line disturbance filtering can be implemented as the next stage in the pipeline structure. The combined characteristics of these first two steps are shown in Figure 14.  
Time-varying RMS recovery is the last step in the processing of EMG signals. The RMS curve is computed as a function of time using a moving window for an EMG signal contaminated with 50 Hz power line noise as shown in Figure 15. The moving windows may overlap and may have a user-defined length. The experience gained in these studies confirms that the disturbance of the 50/60 Hz power line strongly depends on the quality of the electrical contact between the probes (electrodes) and the skin.  
As already emphasized, depending on the position of the surface electrodes and the type of muscle diagnosed, the biopotentials resulting from the heartbeat may overlap the EMG signal. An obvious and general remark may be that the closer the electrode is to the heart, the stronger the influence of the ECG signal, which should be reduced as much as possible. The spectral bands of the ECG and EMG signals are only partially separated, as described in the introduction. Therefore, it should be taken into account that strong filtering of the low-frequency band corresponding to the heart rate may affect the EMG spectrum and consequently, alter the time-dependent RMS plot. Figure 16 shows an example of an RMS signal received for EMG recording with a high-level ECG signal superimposed and the RMS signal after removing the low-frequency spectral band.  
Figure 15: Example of analysis, (**a**) a raw EMG signal of the right abdominal muscle with high content of power line parasitic disturbances, (**b**) RMS-80/40 EMG signal of the right abdominal muscle with high content of power line parasitic disturbances, HP filter (\(f_{c}=10\) Hz), comb 40 th order filter (\(\Delta f=\pm 0.5\) Hz).  
Time-dependent RMS diagrams are computed in the last processing step. Selected examples of RMS curves and for the same EMG input signal for different lengths and shift values are shown in Figure 17. The RMS calculation is a type of low-pass filtering. The window width defined for the RMS calculation affects the cutoff frequency of such a filter. As a result, for a wider window, the RMS peak amplitudes and the details of the higher frequency signal decrease.