Quantification of Epileptic Electrical Sustained Activity during Sleep in BECT Patients

Electrical status epilepticus during sleep (ESES) is a specific EEG phenomenon induced by sleep with near-constant spike and slow wave emission. In the clinical diagnosis of its associated syndromes, the quantification of abnormal electroencephalography (EEG) during sleep, i.e. spike-wave index (SWI), is often used as an important reference standard. It is based on the characteristics of spikes and slow-spikes in the central temporal region. EEG signals can identify the presence of seizures for patients and provide information about the severity and extent of abnormal EEG activity during sleep, which helps doctors to better understand the patient’s physiological function and to develop individualised treatment and prognosis plans. In this paper, we proposed a novel method that combines deep learning and morphological operations to identify and quantify epileptic electrical sustained activity during sleep. The proposed method provides the mean SWI error of 6.04%, the Recall of 87.37% and the Precision of 56.11%. Besides, 15.2%, 64.6% and 85.3% could be achieved for PCT (1%), PCT (5%) and PCT (10%), respectively. The experimental results show that the proposed method has great potential for the clinical diagnosis and prognosis of children with epilepsy. It will help to provide long-range EEG detection for patients with ESES syndrome, thus offering the possibility of early treatment for patients.