  Scalp electroencephalogram (EEG) signals inherently have a low
signal-to-noise ratio due to the way the signal is electrically transduced.
Temporal and spatial information must be exploited to achieve accurate
detection of seizure events. Most popular approaches to seizure detection using
deep learning do not jointly model this information or require multiple passes
over the signal, which makes the systems inherently non-causal. In this paper,
we exploit both simultaneously by converting the multichannel signal to a
grayscale image and using transfer learning to achieve high performance. The
proposed system is trained end-to-end with only very simple pre- and
postprocessing operations which are computationally lightweight and have low
latency, making them conducive to clinical applications that require real-time
processing. We have achieved a performance of 42.05% sensitivity with 5.78
false alarms per 24 hours on the development dataset of v1.5.2 of the Temple
University Hospital Seizure Detection Corpus. On a single-core CPU operating at
1.7 GHz, the system runs faster than real-time (0.58 xRT), uses 16 Gbytes of
memory, and has a latency of 300 msec.
