Abstarct: Electromyography signals (EMGs) interpret the electrical activity of the muscles. These signals include the total potential of all motion units in the field of electrode detection. Surface EMGs are a powerful tool for detecting a wide range of diseases. EMG is widely used in neuroscience, sports medicine, and rehabilitation. When EMG signals are recorded, noise such as detector noise and recording devices, ambient sounds, moving noise, intrinsic instability of biological signals, etc. are received from different sources and affect the recorded EMG. When the EMG is recorded from the uppermost muscle, electrocardiograms (ECGs) have a significant effect on EMG signals, and its analyzes and measurements are unreliable. It is very important to choose an effective way to eliminate the noise of the medical signals and make sure the signal processing is done. Various methods have been proposed to remove ECG noise from EMG signals, but limited studies have made direct comparisons between different methods. The main problems in comparing different researches are the different signals, electrodes and their collection systems. Understanding the impact of ECG removal methods on domain and frequency parameters is critical as it extends the use of EMG. It is also necessary that methods used to measure EMG should be evaluated for potential efficacy and complications. Therefore, our aim is to assess current commonly used perspectives and suggest a suitable way to improve this process. Losing a member, especially a hand or an arm, can damage the quality of a person's life, so the main motive for doing this research is helping the affected person. In this dissertation, a supervised method is proposed to select the appropriate frequency time units baxsed on the characteristics of the sparse coefficients. In the proposed method, ECG and EMG signal samples were modeled first, which is done by training the dictionary and obtaining baxse vectors for each of the signals. After modeling the ECG and EMG signals, the separation was performed for each signal according to the frequency components associated with it. After applying the proposed method in the proposed system, the output results of the system are observed that the proposed method has a significant performance compared to other methods.