Zelmann R, Mari F, Jacobs J, Zijlmans M, Chander R, Gotman J
Department of Biomedical Engineering at McGill University and the Montreal Neurological Institute, Montreal, QC, H3A 2B4, Canada.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:2329-33. doi: 10.1109/IEMBS.2010.5627464.
High Frequency Oscillations (HFOs) in the EEG are a promising biomarker of epileptogenic tissue. Given that the visual marking of HFOs is highly time-consuming and subjective, automatic detectors are necessary. In this study, we present a novel automatic detector that detects HFOs by incorporating information of previously detected baselines. The detector was trained on 72 channels and tested on 278, achieving a mean sensitivity of 96.8% with a mean false positive rate of 4.86%. This low rate is reasonable since only visually marked baseline segments were considered as the true negatives. This detector could be useful for the systematic study of HFOs and for their eventual clinical application.
脑电图中的高频振荡（HFOs）是一种很有前景的致痫组织生物标志物。鉴于HFOs的视觉标记非常耗时且主观，因此需要自动检测方法。在本研究中，我们提出了一种新型自动检测方法，该方法通过合并先前检测到的基线信息来检测HFOs。该检测器在72个通道上进行了训练，并在278个通道上进行了测试，平均灵敏度达到96.8%，平均误报率为4.86%。由于仅将视觉标记的基线段视为真阴性，所以这个低误报率是合理的。该检测器对于HFOs的系统研究及其最终的临床应用可能会很有用。
