**4. EMG decomposition into MUAP**

Raw EMG signal consists of superpositioned motor unit activation potentials (MUAP) and noise components. Muscle crosstalk is a major issue during recording of the biological signals. The crosstalk is dependent on factors such as anatomical site for the placement of electrodes, type of movement, and skin thickness. Since it is harder for sEMG to detect the origin of muscle electrical activity, the chances of muscle crosstalk are higher in sEMG than needle EMG [13]. Besides, low spatial resolution, high movement artifact, and narrow frequency range makes needle EMG more promising as a diagnostic tool in nerve conduction studies for assessing neurological disorders [13]. Changes in the shape of MUAPs, large dynamic range of action potential among motor units and superposition of motor units pose major challenges to decomposing the sEMG.

Fang et al. [34] decomposed EMG into MUAP by wavelet transform. The technique utilized spectrum matching in wavelet domain as opposed to waveform matching. De Luca et al. [35] proposed a method to decompose the sEMG into MUAP during cyclic dynamic contractions. The algorithm solved two main problems, the first associated with the displacement of the electrode on the surface of the skin leading to alteration in the shape of MUAPs, and second regarding lengthening and shortening of the muscle fibers while undergoing those contractions. The algorithm was an extension of the algorithm by Nawab et al. The process was followed as an extracting time-varying time template parameter, performing time-varying filter analysis, clustering on MUAP trains, shape refinement, test, and decomposition. If the test failed, the iterations were done again for shape refinement of MUAPs. Precision Decomposition I (PD I), which was earlier used to decompose needle EMG data was updated to decompose sEMG and referred as PD (III). An updated approach of PD III reported by Nawab et al. has PD-IPUS (Integrated Processing and Understanding) and PD-IGAT (Iterative Generate and Test) [36, 37]. Another method to decompose sEMG into MUAP trains included a hybrid approach of K-means clustering and convolution kernel compensation method. K-means clustering was performed to estimate the pulse trains, which were later updated iteratively by convolution kernel compensation method [38].


#### **Table 1.**

*MUAP abnormalities and indicated anatomical changes.*

The question arises, what changes may a neurological disorder or injury bring to MUAPs? The features of a MUAP (rise time, duration, amplitude, phases/turns, recruitment and, stability) are vital to diagnosing the cause of abnormality in muscle coordination leading to gait or other movement disorders. A normal motor unit and a motor unit after injury (axonal injury) are distinguishable [32, 39–41]. MUAPs from needle EMG are not only adequate in diagnosing neuropathy (nerve injury) but can also determine the severity of the neuropathic condition [41]. Abnormal motor units constitute polyphasic potentials, unlike diphasic or triphasic potentials that exist in healthy individuals. Polyphasic potentials are a result of nascent potentials and terminal collateral sprouting [40]. Rodriguez-Carreno et al. [6] reported MUAPs shape abnormality pertinent to the anatomical phenomena shown in **Table 1**. A study conducted on mice with amyotrophic lateral sclerosis (ALS) using single unit extracellular recording within the spinal cord and EMG revealed gait variability [32]. In ALS mice, the low frequency of motor neuron and irregularities in the motor burst were co-occurring with fractionated EMG.

Among patients with myopathy, short, small, long duration, polyphasic and early recruitment of MUAPs were observed [39]. Different myopathy disorder studies in relation to MUAP trains were conducted using needle EMG by Paganoni et al. [39]. In early phases of disorders due to loss in muscle fibers the compound muscle action potential amplitude is lower. The result was short, small and early recruitment of MUAPs, but in Lambert-Eaton Myasthenic Syndrome, higher CMAP amplitude was observed. The shapes of MUAPs also alter with chronicity. Instead of positive sharp wave and fibrillation in the needle EMG, a mixture of long and short duration of EMG is prevalent [39]. Use of sEMG in comparison to needle EMG for postural disorder is preferable. sEMG is very good at detecting kinesiological disorders such as myotonia, myoclonus and tremors [13]. It can further be decomposed into MUAPs with the PD (III) algorithm, or hybrid of K-means and convolution kernel compensation method.
