  In current clinical practices, electroencephalograms (EEG) are reviewed and
analyzed by trained neurologists to provide supports for therapeutic decisions.
Manual reviews can be laborious and error prone. Automatic and accurate
seizure/non-seizure classification methods are desirable. A critical challenge
is that seizure morphologies exhibit considerable variabilities. In order to
capture essential seizure features, this paper leverages an emerging deep
learning model, the independently recurrent neural network (IndRNN), to
construct a new approach for the seizure/non-seizure classification. This new
approach gradually expands the time scales, thereby extracting temporal and
spatial features from the local time duration to the entire record. Evaluations
are conducted with cross-validation experiments across subjects over the noisy
data of CHB-MIT. Experimental results demonstrate that the proposed approach
outperforms the current state-of-the-art methods. In addition, we explore how
the segment length affects the classification performance. Thirteen different
segment lengths are assessed, showing that the classification performance
varies over the segment lengths, and the maximal fluctuating margin is more
than 4%. Thus, the segment length is an important factor influencing the
classification performance.
