Objective: Electrical status epilepticus during sleep (ESES), as electroencephalographic disturbances, is characterized by strong activation of epileptiform activity in the electroencephalogram during sleep. Quantitative descriptors of such epileptiform activity can support the diagnose and the prognosis of children with ESES. To quantify the epileptiform activity of ESES, a knowledge-based approach to mimic the clinical decision-making process is proposed.
Approach: Firstly, a morphological operations-based scheme is designed to quickly locate the positive peaks/negative pits and roughly estimate the onset/offset of spike and slow-wave abnormalities. Then, to provide the accurate duration of ESES patterns, a set of rules for further adjusting these onsets/offsets are proposed by merging medical knowledge with a generalized threshold obtained from statistics. As such, the quantification is accomplished by evaluating the obtained spike and slow-wave abnormalities and their various durations.
Main results: The effectiveness and feasibility of the proposed method were evaluated on a clinical dataset that collected at Children's Hospital of Fudan University, Shanghai, China. We demonstrate that the proposed method can recognize different types of spike and slow-wave abnormalities. The sensitivity, precision, and false positive rate achieved 91.96%, 97.09%, and 1.88 min-1, respectively. The estimation error for the spike-wave index was 2.32%. Comparison results showed that our method outperforms the state-of-the-art.
Significance: The quantification of spike and slow-waves provides information about ESES activity. The detection of variations types of spike and slow-waves improves the performance in the quantification of ESES. Experimental results suggest that the proposed method has great potential in automatic ESES quantification and can help improve the diagnosis and researches of epileptic encephalopathy with ESES.