Automatic Seizure Prediction using CNN and LSTM
This work addresses the need for automated seizure prediction to assist doctors and reduce clinical workload, representing an incremental improvement in deep learning applications for EEG analysis.
The paper tackled the problem of automatically predicting seizures from EEG signals by proposing an end-to-end deep learning algorithm, achieving an average sensitivity of 97.746% and a false positive rate of 0.2373 per hour on the CHB-MIT dataset.
The electroencephalogram (EEG) is one of the most precious technologies to understand the happenings inside our brain and further understand our body's happenings. Automatic prediction of oncoming seizures using the EEG signals helps the doctors and clinical experts and reduces their workload. This paper proposes an end-to-end deep learning algorithm to fully automate seizure prediction's laborious task without any heavy pre-processing on the EEG data or feature engineering. The proposed deep learning network is a blend of signal processing and deep learning pipeline, which automates the seizure prediction framework using the EEG signals. This proposed model was evaluated on an open EEG dataset, CHB-MIT. The network achieved an average sensitivity of 97.746\text{\%} and a false positive rate (FPR) of 0.2373 per hour.