EEG Epileptic Seizure Classification Using Hybrid Time-Frequency Attention Deep Network

Epileptic seizure is a complex neurological disorder and is difficult to detect. Observing and analyzing the waveform changes of EEG signals is the main way to monitor epilepsy activity. However, due to the complexity and instability of EEG signals, the effectiveness of identifying epileptic region by previous methods using EEG signals is not very satisfactory. On the one hand, these methods use the initial time series directly, which reflect limited epilepsy related features; On the other hand, they do not fully consider the spatiotemporal dependence of EEG signals. This study proposes a novel epileptic seizure classification method using EEG based on a hybrid time-frequency attention deep network, namely, a time-frequency attention CNN-BiLSTM network (TFACBNet). TFACBNet firstly uses a time-frequency representation attention module to decompose the input EEG signals to obtain multiscale time-frequency features which provides seizure relevant information within the EEG signals. Then, a hybrid deep network combining convolutional neural network (CNN) and bidirectional LSTM (BiLSTM) architecture extracts spatiotemporal dependencies of EEG signals. Experimental studies have been performed on the benchmark database of the Bonn EEG dataset, achieving 98.84% accuracy on the three-category classification task and 92.35% accuracy on the five-category classification task. Our experimental results prove that the proposed TFACBNet achieves a state-of-the-art classification effect on epilepsy EEG signals.