  Epilepsy is the most common, chronic, neurological disease worldwide and is
typically accompanied by reoccurring seizures. Neuro implants can be used for
effective treatment by suppressing an upcoming seizure upon detection. Due to
the restricted size and limited battery lifetime of those medical devices, the
employed approach also needs to be limited in size and have low energy
requirements. We present an energy-efficient seizure detection approach
involving a TC-ResNet and time-series analysis which is suitable for low-power
edge devices. The presented approach allows for accurate seizure detection
without preceding feature extraction while considering the stringent hardware
requirements of neural implants. The approach is validated using the CHB-MIT
Scalp EEG Database with a 32-bit floating point model and a hardware suitable
4-bit fixed point model. The presented method achieves an accuracy of 95.28%, a
sensitivity of 92.34% and an AUC score of 0.9384 on this dataset with 4-bit
fixed point representation. Furthermore, the power consumption of the model is
measured with the low-power AI accelerator UltraTrail, which only requires 495
nW on average. Due to this low-power consumption this classification approach
is suitable for real-time seizure detection on low-power wearable devices such
as neural implants.
