## 1 Problem Introduction  
Non-invasive methodologies for the analysis and detection of diseases represent a significant contribution to medical science. At the same time, the development of these methodologies poses new challenges to the scientific community regarding the use of technology, sensor placement, and the evolution of biomedical signal analysis and study. Therefore, methodologies and technologies must always be implementable and easily reproducible. The challenge is to develop powerful and interfaced integrated software-hardware devices for individual use, particularly considering the complexity of biomedical signals, which are derived from electrical signals detected on the human body [1, 2]. Biomedical signals are vital in medical diagnostics because they convey information from biological tissues or organs during normal or pathological functioning. Often, this information is not easily accessible and requires specific acquisition and processing procedures to extract diagnostically and therapeutically relevant information. Biomedical signalsare typically detected using electrodes, which measure the potential difference between two points [3,4]. The automated acquisition and processing of these signals promises to extract physiological and clinical information with greater accuracy. Therefore, there is an urgent need to develop new tools and employ appropriate, non-intrusive processing techniques to achieve this goal. The integration of automation has significantly advanced both diagnostic methodologies and therapeutic interventions [5]. The search for circuit structures that can accommodate exponentially attenuated signals is crucial to advancing electronic device technologies in various fields, particularly biomedical applications. Sensors used in biomedical contexts are of particular interest, given their critical role in monitoring physiological parameters, detecting abnormalities, and facilitating medical interventions [6,7]. Robust and efficient circuit structures are essential to ensuring the accuracy and reliability of data acquisition in these crucial applications. The development of circuit structures capable of processing exponentially attenuated signals is essential to driving advances in electronic device technologies, especially in the biomedical field, where the speed and reliability of data acquisition are of primary importance for diagnostic and therapeutic purposes [8]. One of the most significant challenges in the field of biomedical signal processing is the presence of disturbances and noise that permeate the signal of interest, thus preventing accurate determination of its trajectory. For example, surface electromyographic (sEMG) signals, which transmit the propagation of electrical activity, are typically acquired using electrodes attached to the patient's skin [9]. Electromyography is a methodology that allows the study of the nervous system by recording the electrical muscle signal that propagates during contraction. From a medical perspective, electromyography allows the study of muscle pathophysiology. From an engineering perspective, when skeletal muscle fibres contract, they conduct electrical activity (action potential, AP) that can be measured by electrodes applied to the skin surface above the muscles [10]. The propagation of the potential, in the case of surface electromyography, is acquired through electrodes applied to the patient's skin. The most common surface electrodes are adhesive or Ag/AgCl-type gels, given their characteristics, including low noise (5-10 uV) and ease of manufacture [11]. Needle or wire electrodes are typically made of stainless steel and are mainly used in emergency cases. In humans, the basic action potential is triphasic; the first two phases are similar in width, while the terminal phase has a peak-to-peak width that is only 5-10% of that of the first two phases. In clinical settings, the summation of the action potentials of all the motor units (MUs) in each muscle is detected using a pair of appropriately positioned electrodes.