ScoreNet: A neural network-based post-processing model for identifying epileptic seizure onset and offset in EEGs

This article aims to design an automatic detection algorithm of epileptic seizure onsets and offsets in scalp EEGs. A proposed scheme consists of two sequential steps: the detection of seizure episodes, and the determination of seizure onsets and offsets in long EEG recordings. We introduce a neural network-based model called ScoreNet as a post-processing technique to determine the seizure onsets and offsets in EEGs. A cost function called a log-dice loss that has an analogous meaning to F1 is proposed to handle an imbalanced data problem. In combination with several classifiers including random forest, CNN, and logistic regression, the ScoreNet is then verified on the CHB-MIT Scalp EEG database. As a result, in seizure detection, the ScoreNet can significantly improve F1 to 70.15% and can considerably reduce false positive rate per hour to 0.05 on average. In addition, we propose detection delay metric, an effective latency index as a summation of the exponential of delays, that includes undetected events into account. The index can provide a better insight into onset and offset detection than conventional time-based metrics.