J Neurol Surg A Cent Eur Neurosurg 2014; 75 - p03
DOI: 10.1055/s-0034-1383747

Human Intracranial High Frequency Oscillations (HFOs) Detected by Automatic Time-Frequency Analysis

S. Burnos 1, P. Hilfiker 2, O. Sürücü 3, F. Scholkmann 4, N. Krayenbühl 3, T. Grunwald 2, J. Sarnthein 3
  • 1Institute of Neuroinformatics, ETH Zurich, Zurich, Switzerland
  • 2Swiss Epilepsy Centre, Zurich, Switzerland
  • 3Neurosurgery Department, University Hospital Zurich, Zurich, Switzerland
  • 4Biomedical Optics Research Laboratory, Neonatology Department, University Hospital Zurich, Zurich, Switzerland

Aims: High frequency oscillations (HFOs) have been proposed as a new biomarker for epileptogenic tissue in the iEEG. The exact characteristics of clinically relevant HFOs and their detection are still to be defined.

Methods: We developed a new method for HFO detection1, which we have applied to the intracranial EEG of six patients. The new developed algorithm is based on two signal-processing steps. In a first stage, events of interest (EoIs) in the iEEG were defined by thresholding the signal with respect to energy and duration. To recognize HFOs among the EoIs, in a second stage the iEEG were transformed into the time-frequency domain using the Stockwell transform, and the instantaneous power spectrum was parameterized. The parameters were optimized for HFO detection in patient 1 and tested in patients 2-5. Channels were ranked by HFO rate and those with rate above half maximum constituted the HFO area. The seizure onset zone (SOZ) served as gold standard.

Results: The detector distinguished HFOs from artifacts and other EEG activity such as interictal epileptiform spikes. Computation took few minutes. We found HFOs with relevant power at frequencies also below the 80-500 Hz band, which is conventionally associated with HFOs. The HFO area overlapped with the SOZ with good specificity > 90% for five patients and one patient was re-operated. The performance of the detector was compared with two well-known detectors. HFOs were found in intraoperative recordings under Propofol anesthesia. Channels with intraoperative HFOs lay within the area of interictal spikes.

Conclusions: Compared with methods detecting energy changes in filtered signals, our second stage-analysis in the time-frequency domain discards spurious detections caused by artifacts or sharp epileptic activity and improves the detection of HFOs. The fast computation and reasonable accuracy hold promise for the online implementation the detector during surgery.

References

References

1 Burnos S, et al. PLoS ONE 2014