SyncLearnNet: Generalized Epileptic Seizure Detection Network Based on Brain Signals

Epilepsy is a prevalent neurological disorder with significant detrimental effects on health. Accurate seizure detection is crucial for the precise diagnosis and effective treatment of epilepsy. Brain signals is widely recognized as a reliable clinical tool for diagnosing and evaluating severity of seizures. Traditionally, medical researchers have relied on visual inspection to identify and locate seizures and epileptogenic areas. However, manual analysis of brain data is both subjective and time-consuming. In recent years, there has been a surge in studies focusing on automatic seizure detection algorithms based on brain signals, driven by the advancements in artificial intelligence and digital brain signal technology. Nevertheless, in tackling this task, many of these studies have neglected to leverage the rich implicit information of samples to extract comprehensive feature representation for enhancing model performance. To address this gap, we propose a generalized model called SyncLearnNet for seizure detection based on brain signals. SyncLearnNet incorporates VariaScan and BatchAttention modules designed to fully utilize both intra-sample and inter-sample information, thereby improving feature discrimination without requiring additional data. Furthermore, the introduction of CurriClassifier aims to enhance the model's generalization performance. Experiments conducted on a public human seizure dataset CHB-MIT and a self-built animal seizure dataset comprising data from five rats demonstrated this method outperforms existing seizure detection methods in terms of generalization performance.