This work investigates the application of the Ensemble Empirical Mode Decomposition (EEMD) and the timefrequency techniques for treatment of the electromyography (EMG) signal. The EMG signals are usually corrupted by artifacts that hide useful information then the extraction of high-resolution EMG signals from recordings contaminated with back ground noise becomes an important problem. The Ense...

A novel electromyography (EMG) signal decomposition framework is presented for the thorough and precise analysis of intramuscular EMG signals. This framework first detects all of the active motor unit action potentials (MUAPs) and assigns single MUAP segments to their corresponding motor units. MUAP waveforms that are found to be superimposed are then resolved into their constituent single MUAP...

This study presents a system composed of an electromyostimulation (ES) and an electromyograph (EMG) modules which analyze in real time EMG during ES and proposes a new method based on wavelet decomposition to analyze changes in M wave. It leads to introduce a new muscular fatigue index. Different methods to filter the EMG noise are then compared. The results show that this new index gives relia...

To test whether electromyogram (EMG) is nonlinear deterministic signal or just random noise, we comprehensively analyze four EMGs of an adult woman. At first, we calculate the correlation time, L-Z complexity, approximate entropy (ApEn), maximum Lyapunov exponent (Ly1) and correlation dimension (Dcorr) of each EMG data and its ten surrogate data. We find that all the results are quite different...

TO THE EDITOR: A major constraint in the analysis of motor unit activity in humans is that relatively few active motor units can be detected concurrently due to the high selectivity of needle electrodes commonly used for electromyographic (EMG) recordings. Furthermore, the accurate decomposition of intramuscular EMG signals is usually not possible at high muscle forces due to the many motor uni...

Electromyographic (EMG) signal decomposition is the process of resolving an EMG signal into its constituent motor unit potential trains (MUPTs). The purpose of EMG signal decomposition is to provide an estimate of the firing pattern and motor unit potential (MUP) template of each active motor unit (MU) that contributed significant MUPs to the EMG signal. The extracted MU firing patterns, MUP te...

A major task in the decomposition of multi-channel Electromyographic (EMG) signals into their uni-source pulse train (UPT) components is to resolve pulse superpositions. Even optimal segmental analysis that fits linear combinations of temporally aligned UPT pulses to short data segments gives rise to errors because of pulse evolution and the presence of non-stationary noise. To address such sho...