Abstract
OBJECTIVE: High Frequency Oscillations (HFOs) are being studied as a biomarker of epilepsy, yet it is unknown how various acquisition parameters at different centers affect detection and analysis of HFOs. This paper specifically quantifies effects of sampling rate (FS) and anti-aliasing filter (AAF) positions on automated HFO detection. METHODS: HFOs were detected on intracranial EEG recordings (17 patients) with 5kHz FS. HFO detection was repeated on downsampled and/or filtered copies of the EEG data, mimicking sampling rates and low-pass filter settings of various acquisition equipment. For each setting, we compared the HFO detection sensitivity, HFO features, and ability to identify the ictal onset zone. RESULTS: The relative sensitivity remained above 80% for either FS ⩾2kHz or AAF ⩾500Hz. HFO feature distributions were consistent (AUROC<0.7) down to 1kHz FS or 200Hz AAF. HFO rate successfully identified ictal onset zone over most settings. HFO peak frequency was highly variable under most parameters (Spearman correlation<0.5). CONCLUSIONS: We recommend at least FS ⩾2kHz and AAF ⩾500Hz to detect HFOs. Additionally, HFO peak frequency is not robust at any setting: the same HFO event can be variably classified either as a ripple (<200Hz) or fast ripple (>250Hz) under different acquisition settings. SIGNIFICANCE: These results inform clinical centers on requirements to analyze HFO rates and features.