An attention-based deep learning method for the detection of electrical status epilepticus during sleep from electroencephalogram waveform analysis in children

Electrical status epilepticus during sleep (ESES) is a special interictal electroencephalographic phenomenon observed in children with epilepsy. ESES refers to continuous or nearly continuous epileptiform discharges of bilateral (or unilateral) spikes and waves induced during non-rapid eye movement (NREM) sleep. It mainly manifests in a series of childhood epileptic syndromes characterized by seizures, ESES, and cognitive impairment, which seriously affect the health of children. Typically, the spike-wave index (SWI), which is the percentage of spikes and waves duration in the total NREM time, is an important criterion for diagnosing ESES. The SWI measures the proportion of the total duration of spikes and waves to the NREM sleep time within several hours. Currently, SWI is obtained primarily by manual measurement and calculation, which has a certain subjective bias, low accuracy, time and labor consumption, and is a waste of medical resources. Therefore, we designed a novel data segmentation model based on a multiresolution convolutional neural network and a self-attention mechanism to calculate the SWI quickly and accurately. This can assist clinicians in making more accurate judgments and providing a better guide for the treatment of children with epilepsy. A large amount of clinical patient data was used to test the proposed model. The results confirmed that this model has an acceptable generalization ability for data from different patients and the same patient at different times. Each performance index of the model was superior to that of current common models, thus fulfilling the requirements for clinical testing.