  Although recent studies have proposed seizure detection algorithms with good
sensitivity performance, there is a remained challenge that they were hard to
achieve significantly short detection latency in real-time scenarios. In this
manuscript, we propose a novel deep learning framework intended for shortening
epileptic seizure detection latency via probabilistic prediction. We are the
first to convert the seizure detection task from traditional binary
classification to probabilistic prediction by introducing a crossing period
from seizure-oriented EEG recording and proposing a labeling rule using
soft-label for crossing period samples. And, a novel multiscale STFT-based
feature extraction method combined with 3D-CNN architecture is proposed to
accurately capture predictive probabilities of samples. Furthermore, we also
propose rectified weighting strategy to enhance predictive probabilities, and
accumulative decision-making rule to achieve significantly shorter detection
latency. We implement the proposed framework on two prevalent datasets --
CHB-MIT scalp EEG dataset and SWEC-ETHZ intracranial EEG dataset in
patient-specific leave-one-seizure-out cross-validation scheme. Eventually, the
proposed algorithm successfully detected 94 out of 99 seizures during crossing
period and 100% seizures detected after EEG onset, averaged 14.84% rectified
predictive ictal probability (RPIP) errors of crossing samples, 2.3 s detection
latency, 0.08/h false detection rate (FDR) on CHB-MIT dataset. Meanwhile, 84
out of 89 detected seizures during crossing period, 100% detected seizures
after EEG onset, 16.17% RPIP errors, 4.7 s detection latency, and 0.08/h FDR
are achieved on SWEC-ETHZ dataset. The obtained detection latencies are at
least 50% shorter than state-of-the-art results reported in previous studies.
