  Epilepsy is a prevalent neurological disorder that affects millions of
individuals globally, and continuous monitoring coupled with automated seizure
detection appears as a necessity for effective patient treatment. To enable
long-term care in daily-life conditions, comfortable and smart wearable devices
with long battery life are required, which in turn set the demand for
resource-constrained and energy-efficient computing solutions. In this context,
the development of machine learning algorithms for seizure detection faces the
challenge of heavily imbalanced datasets. This paper introduces EpiDeNet, a new
lightweight seizure detection network, and Sensitivity-Specificity Weighted
Cross-Entropy (SSWCE), a new loss function that incorporates sensitivity and
specificity, to address the challenge of heavily unbalanced datasets. The
proposed EpiDeNet-SSWCE approach demonstrates the successful detection of
91.16% and 92.00% seizure events on two different datasets (CHB-MIT and
PEDESITE, respectively), with only four EEG channels. A three-window majority
voting-based smoothing scheme combined with the SSWCE loss achieves 3x
reduction of false positives to 1.18 FP/h. EpiDeNet is well suited for
implementation on low-power embedded platforms, and we evaluate its performance
on two ARM Cortex-based platforms (M4F/M7) and two parallel ultra-low power
(PULP) systems (GAP8, GAP9). The most efficient implementation (GAP9) achieves
an energy efficiency of 40 GMAC/s/W, with an energy consumption per inference
of only 0.051 mJ at high performance (726.46 MMAC/s), outperforming the best
ARM Cortex-based solutions by approximately 160x in energy efficiency. The
EpiDeNet-SSWCE method demonstrates effective and accurate seizure detection
performance on heavily imbalanced datasets, while being suited for
implementation on energy-constrained platforms.
