Multi-Channel Fusion Deep Wavelet Spectrum Network for Epileptic Signal Classification

Recent developments in the detection of epilepsy and classification of seizures using electroencephalogram (EEG) signals have shown notable progress, yet they still face some challenges. For example, tensor decomposition techniques struggle with high computational demands, and deep learning methods often do not fully utilize the spatial structure of EEG data. This paper presents MavenNet, a Multichannel Wavelet Convolutional Network aimed at improving automated detection of epilepsy and seizure classification. MavenNet begins by applying the continuous wavelet transform to represent the multichannel temporal spectrum as a third-order tensor, which is then processed through multichannel convolution operations. To enhance the interpretability of the model, Class Activation Mapping (CAM) is used to visualize the spectrogram features that are essential for making classification decisions. Experimental results from three widely used datasets and one private dataset indicate that MavenNet outperforms leading algorithms. The proposed model maintains the spatial structure of EEG signals and increases the transparency and reliability of classification outcomes, positioning it as a valuable tool for clinical diagnosis of epilepsy.