Research on Seizure Detection Using EEG Signals Based on Multi-Scale Convolutional Neural Networks BiLSTM-Multi-Head Self-Attention

Traditional methods for epileptic seizure detection suffer from limitations such as insufficient feature extraction capability, high computational complexity, and inadequate generalization performance. In this study, leveraging Electroencephalogram (EEG) signals, a novel epileptic seizure detection method based on the Multi-Scale Convolutional Neural Networks-BiLSTM-Multi-Head self-attention(MSCNN-BiLSTM-MHSA) model is proposed. The MSCNN-BiLSTM-MHSA model comprehensively extracts features of EEG signals at different scales by constructing an improved multi-scale convolutional neural network. Furthermore, the introduction of the multi-head self-attention mechanism enables the model to focus more on key features during the iteration process, thereby enhancing the feature learning capability. Experiments were conducted on two epileptic datasets, namely CHB-MIT and Bonn, for validation. The results demonstrate that the MSCNN-BiLSTM-MHSA model achieves an accuracy of 96.23% and 96.13% respectively in epileptic seizure detection.