APPLICATION OF DEEP LEARNING ALGORITHMS IN MEDICAL DIAGNOSIS BASED ON BIOPHYSICAL SIGNALS (EEG/EMG)

• Muhammadkodir Sharifjonov

  Tashkent State Medical University

  Author

• Abdulaziz Valiyev

  Tashkent State Medical University

  Author

• Muhriddin Suyunov

  Tashkent State Medical University

  Author

• Shavkat Kholmetov

  Assistant, Department of Biomedical Engineering, Informatics and Biophysics, Tashkent State Medical University

  Author

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

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