Background: Interictal epileptiform discharges
(IEDs) are known as epilepsy biomarkers for seizure detection, and It is
essential for clinicians to detect them from from physiological events with
similar temporal frequency characteristics. Methods:
We analyzed the SEEG recordings obtained from patients with
medically-resistant epilepsy (MRE) implanted with DE at the Western
University Hospital Epilepsy Unit. The data were cleaned, denoised, montaged
and segmented based on the clinical annotations, such as sleep intervals and
observed Ictals. For event detection, the signal waveform and its power were
extracted symmetrically in non-overlapping intervals of 500 ms. Each
waveform’s power across all detected spikes was computed and clustered based
on their energy distributions. Results: The
recordings included thirteen sessions of 24 hours of extracellular
recordings from two patients, with 312 hours extracted from four hippocampus
electrodes anterior and posterior hippocampus. Our results indicate IEDs
carrying the most different characteristics in the bands [25-75] Hz; SWR, on
the other hand, are distributed between [80-170] Hz.
Conclusions: Our algorithm detected and
successfully distinguished IED from SWRs based on their carrying energy
during non-sleep periods. Also, the most powerful spectral features that
they were distinguished from occur in [15-30] Hz and [75-90] Hz.