  Epilepsy is a prevalent neurological disorder that affects approximately 1%
of the global population. Around 30-40% of patients do not respond to
pharmacological treatment, leading to a significant negative impact on their
quality of life. Closed-loop deep brain stimulation (DBS) is a promising
treatment for individuals who do not respond to medical therapy. To achieve
effective seizure control, algorithms play an important role in identifying
relevant electrographic biomarkers from local field potentials (LFPs) to
determine the optimal stimulation timing. In this regard, the detection and
classification of events from ongoing brain activity, while achieving low power
through computationally unexpensive implementations, represents a major
challenge in the field. To address this challenge, we here present two
lightweight algorithms, the ZdensityRODE and the AMPDE, for identifying
relevant events from LFPs by utilizing semantic segmentation, which involves
extracting different levels of information from the LFP and relevant events
from it. The algorithms performance was validated against epileptiform activity
induced by 4-minopyridine in mouse hippocampus-cortex (CTX) slices and recorded
via microelectrode array, as a case study. The ZdensityRODE algorithm showcased
a precision and recall of 93% for ictal event detection and 42% precision for
interictal event detection, while the AMPDE algorithm attained a precision of
96% and recall of 90% for ictal event detection and 54% precision for
interictal event detection. While initially trained specifically for detection
of ictal activity, these algorithms can be fine-tuned for improved interictal
detection, aiming at seizure prediction. Our results suggest that these
algorithms can effectively capture epileptiform activity; their light weight
opens new possibilities for real-time seizure detection and seizure prediction
and control.
