Design of an Intelligent Device for Neuromuscular Functional Connectivity Detection
The development of an intelligent device for detecting neuromuscular functional connectivity represents a cutting-edge innovation in medical diagnostics. This device leverages advanced technologies such as machine learning, signal processing, and neuroinformatics to analyze the functional interactions between the nervous system and muscular systems.
Amyotrophic Lateral Sclerosis (ALS) is one of the most common neuromuscular diseases, characterized by unknown mechanisms and causes. In its early stages, ALS often lacks visible symptoms, and current diagnostic methods frequently face challenges in distinguishing it from other neuromuscular disorders. Early diagnosis of ALS can significantly improve symptom management, enhance treatment strategies, and potentially extend the patient’s lifespan.
In response to this critical need, the research team led by Dr. Amir Homayoun Jafari, Head of the Department of Physics and Medical Engineering at Tehran University of Medical Sciences, has developed a laboratory prototype of an intelligent device for automated detection of neuromuscular functional connectivity. This project was conducted at the Signal and Medical Equipment Laboratory within the Biomedical and Robotic Technology Research Center at the university.
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