Decoding Seizures: A Multiclass Machine Learning Approach

Epileptic seizures are neurological disorders caused by sudden, uncontrolled electrical disturbances and activities in the brain, leading to changes in behavior, movements, or feelings, and, in some cases, loss of consciousness. Electroencephalogram (EEG) signals are utilized in the medical field to diagnose epileptic seizures. Accurate and timely detection of these seizures is crucial for the effective management and treatment of patients. The study presents a robust machine learning system to do multiclass classification of epileptic seizures using EEG data. The study uses datasets from Bonn University, which include five different sets representing various brain states of healthy and epileptic individuals. After preprocessing and normalizing the data, the features are extracted using techniques like power spectral density (PSD) and wavelet transforms. Various classification algorithms like Random Forest, Support Vector Machine, Naive Bayes, and Decision Tree were evaluated through extensive hyperparameter tuning and cross-validation. The Random Forest model emerged as the best classifier, achieving a significant accuracy of 89% in classifying the data into five classes, showing its effectiveness in distinguishing between different classes of seizures. This approach shows significant promise for enhancing the accuracy of epilepsy classification and optimizing treatment strategies.