(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 10, No. 3, 2019 383 | Page www.ijacsa.thesai.org Towards Implementing Framework to Generate Myopathic Signals Amira Dridi 1* , Jassem Mtimet 2* , Slim Yacoub 3* * Signal, Image and Technology of Information Laboratory, National Engineering School of Tunis Tunis el Manar University, BP 37 Belvedere, 1002, Tunis, Tunisia Abstract—In this paper, we describe a simulation system of myopathicsurface electromyography (sEMG) signals. The architecture of the proposed system consists of two cascading modules. SEMG signals of three pathological skeletal muscles (Biceps Brachii, InterosseursDorsalis, TibalisAnterior) were generated. Root Mean Square (RMS Envelope) and Power Spectral Density (PSD) were used to validate our system. Keywords—Component; Surface Electromyography (sEMG); myopathy; root mean square; Power Spectral Density (PSD; skeletal muscles; biceps brachii; interosseous dorsalis; tibialis anterior I. INTRODUCTION Electromyography covers the study of muscle function through electrical signals. This medical examination collects measures and records the electrical signal that propagates in the nerves or in the muscle fibers (Action Potential). It consists of plotting the variations of the muscular membrane on the display screen; this diagnostic procedure is performed either in a non-invasive manner using skin contact electrodes (surface electromyography) or in an invasive manner using needle electrodes (invasive electromyography). These detection processes are often used in several fields such as: neuromuscular clinical diagnostics, rehabilitation, prosthesis control, muscle fatigue studies and gait analysis [1-4]. The mathematical modeling of surface electromyography (sEMG) is a method which allows to synchronize physiological parameters (e.g. recruitment frequency, conduction rate...) with simulated results in order to analyze their influences and to test the validity of the algorithms used to process this kind of signals [5-7]. Recently, research studies have focused on different approaches to modeling and to simulating sEMG signals, which are based on phenomenological as well as physiological aspect [8-10]. In [1] and [6], the authors propose an in-depth recapitulative study of these approaches. Myopathic diseases are disorders in which skeletal muscle is mainly involved. Several factors can cause myopathies including inherited genetic defects (e. g. muscular dystrophies), endocrine, inflammatory or metabolic abnormalities. The different myopathies lead weakness and atrophy of skeletal muscles. Other symptoms of myopathy include fatigue, stiffness, and muscle cramps [11]. Some myopathies, such as muscular dystrophies, develop very early, while others develop later in patient life. Some of them gradually worsen over time and do not respond well to treatment, while others appear treatable and often remain stable for long periods of time [1]. There are no several studies interested to model this kind of signals. However, their generation provides a significant contribution in several areas. For example, for classification purposes, a clinical study is required to build a classification model that is costly in terms of time and resources. In the interest of processing these signals, we propose a model that can be used to generate myopathic signals for different types of skeletal muscles. The paper will be organized as follows: Section 2 presents the components of the myopathicsEMG signal generation system. Section 3 illustrates the experimental results of the proposed simulation model. Finally, in Section 4 we close with a brief conclusion. II. MATERIALS AND METHODS The physiological and anatomy studies of striated skeletal muscles reveal their composition in motor units (MU), which are composed of motoneurons and muscle fibers. In this section, we present a mathematical-based model which generates the electrical activity of myopathic muscular pathologies. The below diagrams (Fig. 1 and Fig. 2) represent the different components of our generation model. A. Intracellular Action Potential Generation The generation of the intracellular action potential (IAP) produces a transmembrane ionic current I m (t) that propagates along the outer membrane of muscle fiber (sarcolemma). Moreover, the fiber is considered as a propagation tube for axially circulating current [9]. We use the following formula to generate the aforementioned current [17]:   ()    ()  (   ) (          )   (1) With:  A, C: constants affecting the amplitude of I m   : Scale factor for adapting the model to the real observations  v: speed of current propagation along the fibers Consequently, myopathic IAPs characterizing by a short duration and low amplitude are produced after a values modification in the responsible parameters of this phenomenon (A,).