Multi-dimensional hybrid bilinear CNN-LSTM models for epileptic seizure detection and prediction using EEG signals

Automatic detection and prediction of epilepsy are crucial for improving patient care and quality of life. However, existing methods typically focus on single-dimensional information and often confuse the periodic and aperiodic components in electrophysiological signals.

We propose a novel deep learning framework that integrates temporal, spatial, and frequency information of EEG signals, in which periodic and aperiodic components are separated in the frequency domain. Specifically, we calculated the periodic and aperiodic components in single channel and the synchronization index of each component between channels. A self-attention mechanism is employed to filter single-channel features by selectively focusing on the most distinguishing features. Then, a hybrid bilinear deep learning network is utilized to capture the spatiotemporal features by combining a convolutional neural network (CNN) and a long short-term memory (LSTM) network. Finally, a bilinear pooling layer is employed to extract second-order features based on interactions between these spatiotemporal features.

The model achieves exceptional performance，with a detection accuracy of 98.84% on the CHB-MIT dataset, and a prediction accuracy of 98.44% on CHB-MIT and 97.65% on the Kaggle dataset, both with an false positive rate (FPR) of 0.02.

This work paves the way for developing real-time, wearable epilepsy prediction devices to improve patient care.