  Objective: The surgical resection of brain areas with high rates of visually
identified high frequency oscillations (HFOs) on EEG has been correlated with
improved seizure control. However, it can be difficult to distinguish normal
from pathological HFOs, and the visual detection of HFOs is very
time-intensive. An automated algorithm for detecting HFOs and for wide-band
spectral analysis is desirable.
  Methods: The damped-oscillator oscillator detector (DOOD) is adapted for HFO
detection, and tested on recordings from one rat and one human. The rat data
consist of recordings from the hippocampus just prior to induction of status
epilepticus, and again 6 weeks after induction, after the rat is epileptic. The
human data are temporal lobe depth electrode recordings from a patient who
underwent pre-surgical evaluation.
  Results: Sensitivities and positive predictive values are presented which
depend on specifying a threshold value for HFO detection. Wide-band
time-frequency and HFO-associated frequency spectra are also presented. In the
rat data, four high frequency bands are identified at 80-250 Hz, 250-500 Hz,
600-900 Hz and 1000-3000 Hz. The human data was low-passed filtered at 1000 Hz
and showed HFO-associated bands at 15 Hz, 85 Hz, 400 Hz and 700 Hz.
  Conclusion: The DOOD algorithm is capable of high resolution time-frequency
spectra, and it can be adapted to detect HFOs with high positive predictive
value. HFO-associated wide-band data show intricate low-frequency structure.
  Significance: DOOD may ease the labor intensity of HFO detection. DOOD
wide-band analysis may in future help distinguish normal from pathological
HFOs.
