APPLICATION OF MACHINE LEARNING IN THE ANALYSIS OF BIOELECTRICAL SIGNALS (EEG, ENG, EMG)

Abstract

This article discusses the application of Machine Learning algorithms in the analysis of electroencephalography (EEG), electroneurography (ENG), and electromyography (EMG) signals. Based on EEG signals, Machine Learning algorithms are used for early detection of epileptic seizures, assessment of brain activity after stroke, and identification of changes in brain rhythms in Parkinson’s and Alzheimer’s diseases. This enables early diagnosis and timely initiation of treatment. In EMG analysis, Machine Learning is widely applied to detect muscle diseases (myopathies), peripheral nerve injuries, and neuromuscular junction disorders. Machine Learning–based analysis of ENG signals is used to assess peripheral nerve conduction, and for early detection and prevention of diabetic neuropathy, traumatic nerve injuries, and demyelinating diseases.