Adaptive Bi-LSTM-based Epileptic Seizure Prediction from EEG Signals Using Deep Learning Algorithm
Keywords:
Epileptic seizure, Bi-LSTM, Electroencephalogram (EEG), convolutional neural network (CNN)Abstract
Millions of people worldwide who suffer from epilepsy are severely impacted by frequent, erratic seizures. Preventing seizures from occurring could greatly enhance patient care and safety. This paper presents an approach to real-time epileptic seizure prediction utilizing a unique kind of neural network known as Bidirectional Long Short-Term Memory (Bi-LSTM). The existing multidimensional CNN deep neural network models have average accuracy for multi-channel EEG data in predicting seizures. The proposed model was selected for its excellent ability to interpret complex seizure patterns from both historical and prospective data. We took advantage of a big dataset of real-time EEG recordings of brain activity. The testing yielded the results with the approach of 98% accuracy in predicting seizures. Using the Bi-LSTM model in real-time systems has shown that it can make precise predictions quickly, offering hope for improving the lives of those with epilepsy.
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