Epilepsy Seizure Detection and Prediction using an Approximate Spiking Convolutional Transformer

arXiv:2402.09424v1 Announce Type: cross
Abstract: Epilepsy is a common disease of the nervous system. Timely prediction of seizures and intervention treatment can significantly reduce the accidental injury of patients and protect the life and health of patients. This paper presents a neuromorphic Spiking Convolutional Transformer, named Spiking Conformer, to detect and predict epileptic seizure segments from scalped long-term electroencephalogram (EEG) recordings. We report evaluation results from the Spiking Conformer model using the Boston Children's Hospital-MIT (CHB-MIT) EEG dataset. By leveraging …

abstract arxiv cs.cv cs.lg detection disease eess.sp epilepsy health life neuromorphic paper patients prediction protect reduce transformer treatment type