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. Author manuscript; available in PMC: 2017 Sep 1.
Published in final edited form as: Clin Neurophysiol. 2016 Jul 15;127(9):3042–3050. doi: 10.1016/j.clinph.2016.06.029

Figure 7. Automated identification of ictal onset zone.

Figure 7

Ictal onset zone is predicted by identifying which, if any, electrodes have anomalously-high HFO rates. Results are shown per patient and per sampling rate (A) and anti-aliasing filter position (B). Algorithm-identified ictal onset (IOZ) channels are classified as either being fully within the resected volume (RV), not fully within the RV, or in some cases, no channels were identified. Even though several patients’ answer change with different settings, there were never any predictions outside the RV, which would have corresponded to a false positive prediction in these patients with class I outcome. Note that because the algorithm was performed without any patient-specific tuning, sometimes higher resolution does not improve the ability to predict IOZ due to higher baseline HFO rates (e.g. UM-08).