Abstract: Epilepsy, a severe neurological disorder, is identified by analyzing intricate brain signals generated by interconnected neurons, often monitored through EEG and ECoG. These signals, characterized by complexity, noise, and non-linearity, pose significant challenges for seizure detection. However, recent strides in machine learning have facilitated the development of robust classifiers capable of effectively analyzing EEG and ECoG data. By leveraging these advancements, researchers can accurately detect seizures and extract pertinent patterns, thereby aiding in the diagnosis and management of epilepsy. Machine learning techniques empower clinicians to uncover valuable insights into the condition, ultimately enhancing patient care and treatment strategies.The integration of machine learning with EEG and ECoG analysis holds promise for advancing our understanding of epilepsy and improving patient outcomes.
Keywords: Seizure detection, data preprocessing, training the model, EEG signals, LSTM model, machine learning.
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DOI:
10.17148/IJARCCE.2024.13386
