DSMN-ESS: Dual-Stream Multitask Network for Epilepsy Syndrome Classification and Seizure Detection

Simultaneous childhood epilepsy syndrome classification and seizure detection are both significant in epilepsy analysis. Current research mainly focuses on a single task, mostly on seizure detection. In this paper, a novel dual-stream multi-task network (DSMN) exploiting multi-channel scalp electroencephalograms (EEGs) is developed to simultaneously perform epilepsy syndrome classification (ESC-Task) and seizure detection (SD-Task), in short as DSMN-ESS. The close correlation between ESC-Task and SD-Task is explored to achieve better performance. To improve the performance, an information sharing gate module is designed in DSMN to enable both tasks to fully obtain the useful information. Meanwhile, a channel weight update module is developed to well extract the internal spatial relationship between multi-channel EEGs. Further, an area-under-the-curve (AUC) based loss is proposed to address the data imbalance issue in epilepsy analysis. Studies on EEG data recorded 49 patients from the Children's Hospital, Zhejiang University School of Medicine (CHZU), are carried out to show the effectiveness of DSMN-ESS. The results show that DSMN-ESS can achieve the highest AUC, 99.95% and 99.78% in ESC-Task and SD-Task, respectively, which are superior over several state-of-the-art (SOTA) methods.