APPLICATION OF DEEP LEARNING ALGORITHMS IN MEDICAL DIAGNOSIS BASED ON BIOPHYSICAL SIGNALS (EEG/EMG)
Keywords:
Biophysical signals, EEG, EMG, deep learning, artificial intelligence, medical diagnosis, neural networks, automated analysis, neurological disorders, neuromuscular disorders.Abstract
Biophysical signals such as electroencephalography (EEG) and electromyography (EMG) provide critical insights into the functional state of the nervous and muscular systems. Accurate interpretation of these signals is essential for diagnosing neurological and neuromuscular disorders, including epilepsy, sleep disorders, movement disorders, and peripheral neuropathies. Traditional manual analysis of EEG and EMG recordings is time-consuming and subject to inter-observer variability. Recent advances in artificial intelligence (AI) and deep learning have enabled automated analysis of biophysical signals, allowing rapid and accurate identification of pathological patterns. This paper reviews current deep learning methodologies applied to EEG and EMG data, discusses challenges such as signal variability, noise, and limited annotated datasets, and explores the potential of AI-assisted systems to enhance diagnostic
