# Multi-Channel Surface EMG Spatio-Temporal Image Enhancement Using Multi-Scale Hessian-Based Filters
## 1 Introduction  
Human muscle is made up of a certain number of motor units (MU) which are comprised of a set of muscle fibers innervated by the motor neuron. The electrical activity of these motor units is called motor unit action potential (MUAP) [1]. The sum of these potentials for a muscle is called electromyograph (EMG). Surface EMG (sEMG) signals are recorded by placing the electrodes on muscle surface [2]. Multi-channel EMG signals are often recorded using a linear array of electrodes [3]. These multi-channel sEMG signals can be represented using spatio-temporal two-dimensional (2D) images. Rows and columns of these images represent the sEMG electrodes and the time samples respectively, while the gray level corresponds to the sEMG amplitude [3, 4, 5]. Representing sEMG signals as 2D images can enable the use of state-of-the-art digital image processing techniques for further processing and features extraction. High-density and multi-channel sEMG signals are useful in muscle geometry estimation, detection of muscle fibers alignment, motor unit length, and innervation zones [3]. Accurate detection and estimation of these muscle's parameters depend on the precise localization of MUAPs in sEMG images. The shape of the MUAP in sEMG images is distorted by noise and interference due to nearby MUAPs, which make it difficult to detect and estimate these parameters [3,4]. Due to interference of the neighbor MUAPs, the MUAP structure has different gray levels and it is difficult to be distinguished from the background noise (see Figure 1a,b) [2]. sEMG signals have varying amplitudes due to variations in the size of mucosa in case of external anal sphincter (EAS) muscle or pinnation of the fibers in case of skeletal muscles [3]. Moreover, different noise sources can contaminate the sEMG signals, which ultimately affect the MUAP structure (see Figure 1b) [6,7]. The spatio-temporal 2D sEMG images of the MUAP propagation structure resembles a line-like structure with either a single line or a v-like shape. The MUAP structure needs to be detected  
Figure 1: Multi-channel surface electromyography (sEMG) signals recorded from external anal sphincter (EAS) using 16 equally spaced electrodes. The signals are acquired from EAS muscle of 478 pregnant women at clinical partners from five European Countries (Germany, Italy, Latvia, Slovenia, Ukraine). Each clinical partner obtained the approval from the local ethical committee. Each subject was informed about the study protocol and singed an informed consent prior to the experiment (**b**) interpolated spatio-temporal digital image representation of the sEMG signals shown in (**a**). before estimating the muscle's physiological and anatomical parameters accurately. The sEMG signals are affected by different factors such as sampling frequency in space and time, the electrode size, interpolation, and truncation effects, and the interelectrode distance [8]. Moreover, sEMG signals are also affected due to severe crosstalk between deep and superficial muscle activation [9]. Due to these limitations, preprocessing of sEMG is needed before further processing. The visualization of the MUAP structure is significantly improved in the preprocessed enhanced spatio-temporal images. This facilitates the segmentation of the MUAP regions due to increased difference between the background and foreground. It also improves the binarization and edge detection. A review of the literature reveals significant research activity in applying digital image processing techniques to sEMG spatio-temporal images. These techniques are used to enhance the sEMG spatio-temporal images and detect the MUAP structure automatically. Authors in Reference [3] used the 2D cross-correlation technique to detect the MUAP and motor unit innervation zone (MU IZ) from sEMG images. In References [10,11], the authors used digital image processing techniques to automatically detect MU IZ and to estimate muscle fiber conduction velocity.