CMEpiNet improved seizure detection sensitivity, reduced the false alarm rate, and maintained stable performance under perturbations on the SeizeIT2 dataset.
CMEpiNet offers an improved multimodal machine learning approach for epilepsy detection using complex-valued convolutions and semantic alignment.
Existing seizure detection methods cannot fully exploit the spatiotemporal features of multimodal signals. They also fail to capture deep associations among cross-modal features. This limits their ability to learn unified representations of spatiotemporal dependencies. This work proposes CMEpiNet (Complex-valued Multimodal Epilepsy detection Network model) to address this issue. CMEpiNet first uses complex-valued convolutions for feature extraction. It explicitly models phase synchronization, phase shifts, and cross-frequency coupling. Thus, EEG, ECG, and EMG features are represented in the complex-valued domain. During feature fusion, CMEpiNet uses a two-level semantic alignment-based fusion method. It applies cross-modal consistency constraints in a shared alignment space. It also performs distribution-level alignment in an epilepsy-related semantic latent space. These operations ensure the consistency of multimodal features in the global semantic structure. Finally, CMEpiNet uses a spatial attention-guided 3D convolutional classifier. The classifier jointly models the temporal, feature, and modality dimensions. Experimental results on the SeizeIT2 dataset show that CMEpiNet improves seizure detection sensitivity, reduces the false alarm rate, and maintains stable performance under perturbations.
Su et al. (Thu,) conducted a other in Epilepsy. CMEpiNet (Complex-valued Multimodal Epilepsy detection Network model) vs. Existing seizure detection methods was evaluated on Seizure detection sensitivity, false alarm rate, and stability under perturbations. CMEpiNet improved seizure detection sensitivity, reduced the false alarm rate, and maintained stable performance under perturbations on the SeizeIT2 dataset.