Machine Learning-Based Early Seizure Detection: A Random Forest Classifier Approach for Pre-Ictal Stage Prediction in Epilepsy

Abrupt and irregular spikes in the brain's electrical activity are the cause of seizures. This could cause transient aberrant behaviors, sensations, or altered states of awareness or unconsciousness. Movements could include jerky, alternatively tensed arms and legs. One of the most prevalent neurological conditions worldwide, epilepsy affects almost 50 million individuals worldwide, according to a report. Over 25% of epileptic individuals experience uncontrollable seizures even with existing medication and surgical treatment options. Preventing Sudden Unexpected Death in Epilepsy (SUDEP) is aided by early seizure detection. A machine learning methodology for seizure detection was introduced by the proposed model.The recognized seizure is diagnosed using the Random Forest Classifier algorithm. Using datasets from the ECG (electrocardiogram) and EEG (electroencephalogram), a pre-trained model is created using supervised and machine learning techniques. Finding seizures in the pre-ictal period is the aim. Using the datasets that are available, the model is trained, demonstrating that there are several causes of epilepsy. The answer is the same despite these variations in the causes. The model predicts and alerts carers, nurses, and hospital administration to an upcoming seizure. For doctors, this early warning is very helpful in enabling timely treatment.