Abstract
High frequency oscillations (HFOs) are novel biomarkers of epileptogenic tissue. Visual identification of HFO in long-term EEG recordings is time consuming due to low HFOs rate, low signal-to-noise ratio and presence of biological and technical artifacts. In this study, we have examined several algorithms of HFOs detection to facilitate analysis of intracranial recordings and increase their diagnostic yield. We have evaluated three newly designed and three published HFOs detectors. Detectors were applied on datasets containing HFOs labeled by experienced readers and their performance evaluated. Results of the detection and properties of the algorithms are reviewed and discussed in respect to clinical practice and their possible utilization during the diagnostic workup in patients with epilepsy.
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