# Processing of EMG Signals with High Impact of Power Line and Cardiac Interferences  
[PERSON] 1  
1 Institute of Applied Computer Science, Lodz University of Technology, 90-924 Lodz, Poland; [EMAIL_ADDRESS] (K.S. ); [EMAIL_ADDRESS] (D.S.) 2 Polish Mother's Memorial Hospital Research Institute, 93-338 Lodz, Poland; [EMAIL_ADDRESS]
3 Institute of Electronics, Lodz University of Technology, 90-924 Lodz, Poland; [EMAIL_ADDRESS] 3  
[PERSON]  
2 Polish Mother's Memorial Hospital Research Institute, 93-338 Lodz, Poland; [EMAIL_ADDRESS]  
[PERSON]  
3 Institute of Electronics, Lodz University of Technology, 90-924 Lodz, Poland; [EMAIL_ADDRESS] 3  
[PERSON]  
4 Institute of Automatic Control, Lodz University of Technology, 90-924 Lodz, Poland; [EMAIL_ADDRESS] (P.C. ); [EMAIL_ADDRESS] (K.T. ); [EMAIL_ADDRESS] (G.L.) [PERSON]  
4 Institute of Automatic Control, Lodz University of Technology, 90-924 Lodz, Poland; [EMAIL_ADDRESS] (P.C. ); [EMAIL_ADDRESS] (K.T. ); [EMAIL_ADDRESS] (G.L.) [PERSON]  
4 Institute of Applied Computer Science, Lodz University of Technology, 90-924 Lodz, Poland; [EMAIL_ADDRESS] (P.C. ); [EMAIL_ADDRESS] (K.T. ); [EMAIL_ADDRESS] (G.L.) [PERSON]  
4 Correspondence: [EMAIL_ADDRESS], Tel. +48-42-631-27504484  
improving detection techniques to reduce noise, and improving EMG signal registration accuracy [4; 5]. Thus, many researchers have used different types of advanced methodologies, including Least Mean Square (LMS) filtering [6], wavelet transform, Wigner-Ville distribution, independent component analysis, empirical mode decomposition and higher-order statistics, for analyzing the EMG signal appropriately [7; 8; 9]. Frequency analysis is widely used for processing biomedical signals in various applications. Among them, high-order filtering and the Fourier Transform (FT), including the Short-time FT, are applied both for analysis and modelling [10]. However, it is quite important to investigate the actual problems of EMG signals analysis and justify the accepted measures because the technology of EMG recording is relatively new. There are still limitations in surface electromyography (sEMG), and there is no general approach for different muscle signal registration. Recent advances in signal processing and mathematical models have made it practical to develop advanced EMG detection and analysis techniques. The primary function of the electro-diagnostic system is to record biological signals faithfully. To this end, it is important to have an optimal'signal to noise ratio', i.e., amplify the neuro-physiological signal voltage while attenuating background noise. This is done using analog hardware and digital signal processing techniques. Many EMG control systems are currently available in the market, for instance, NeuroTrac(r) MyoPlus2 [11] or Baseline Load Cell MMT [12]. The full list of modern electromyographs is available at [13]. However, these EMG acquisition systems allow the processed data to be obtained based on implemented algorithms. Generally, the descriptions of the applied algorithms are not available. According to the authors' knowledge, there is no completed analysis of the applied signal processing algorithms in such acquisition systems or estimation of their efficiency. Electromyography is a diagnostic procedure that evaluates muscle health conditions and the nerve cells that control them. These nerve cells are known as motor neurons. They transmit electrical signals that cause muscles to contract and relax. An EMG translates these signals into graphs or numbers, helping doctors to make a diagnosis [14]. A doctor will usually order an EMG diagnostic test when a patient is showing symptoms of a muscle or nerve disorder. These symptoms may include tingling, numbness, or unexplained weakness in the limbs. EMG results can help the doctor diagnose muscle disorders, nerve disorders, and disorders affecting the connection between nerves and muscles [15]. EMG is not only used in medical diagnostic procedures.