EEG Signal Based Epileptic Seizure Forecasting Using Deep Learning Models

Epilepsy is categorized as a seizure disorder that causes unpredictable chronic seizures. Electroencephalogram (EEG) records electrical activities of brain that can be used to detect epileptic seizures. The EEG data of an epileptic patient includes a vast number of EEG details that may involve timeful manual interpretations. Automatic EEG signal interpretation with sophisticated signal processing techniques plays a major role in detecting epilepsy from EEG recordings. In this study, a new approach of feature extraction for forecasting onset of epileptic seizures is introduced. For analytical processing, the EEG signals are decomposed using Tunable Q Factor Wavelet Transformation. The Statistical Features (SF), Temporal Features (TF) and Global Features (GF) for the sub-bands are extracted using Auto Encoders, Continuous Bag of Words (CBoW) for Signals and Principal Component Analysis (PCA). The EEG signals classification is performed using ANN classifier to distinguish between the normal, inter-ictal and ictal classes.