A Hybrid Convolutional, Mamba and Spiking Neural Network Architecture Search for Seizure Prediction

In recent years, deep learning technology has played an increasingly important role in seizure prediction based on electroencephalogram signals. However, the performance of deep learning algorithms is often highly dependent on the design of their neural network architectures; the process of manually designing neural network architectures is usually very time-consuming and resource-intensive. In addition, different types of networks have distinct advantages, and how to automatically hybridize these networks to design an effective network for seizure prediction remains a substantial challenge. To address this issue, this paper proposes a hybrid convolutional, Mamba, and spiking neural network architecture search framework (CMS-NAS) based on a multiobjective evolutionary algorithm for seizure prediction, which can automatically design a lightweight network capable of comprehensively extracting electroencephalogram features. By simulating the natural selection mechanism, CMS-NAS iteratively evolves candidate networks through populations and uses a multiobjective scoring function to simultaneously optimize the model's prediction accuracy and model size. Extensive experiments are conducted on 2 public datasets and a private dataset, and the experimental results show that the network model searched by the CMS-NAS algorithm achieves competitive performance in seizure prediction.