Department of Automatic Control (ESAII), Biomedical Engineering Research Center (CREB), Universitat Politènica de Catalunya (UPC), Barcelona, Spain. Biomedical Research Networking center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain.
J Neural Eng. 2017 Aug;14(4):046013. doi: 10.1088/1741-2552/aa684c.
In epilepsy, high-frequency oscillations (HFOs) are expressively linked to the seizure onset zone (SOZ). The detection of HFOs in the noninvasive signals from scalp electroencephalography (EEG) and magnetoencephalography (MEG) is still a challenging task. The aim of this study was to automate the detection of ripples in MEG signals by reducing the high-frequency noise using beamformer-based virtual sensors (VSs) and applying an automatic procedure for exploring the time-frequency content of the detected events.
Two-hundred seconds of MEG signal and simultaneous iEEG were selected from nine patients with refractory epilepsy. A two-stage algorithm was implemented. Firstly, beamforming was applied to the whole head to delimitate the region of interest (ROI) within a coarse grid of MEG-VS. Secondly, a beamformer using a finer grid in the ROI was computed. The automatic detection of ripples was performed using the time-frequency response provided by the Stockwell transform. Performance was evaluated through comparisons with simultaneous iEEG signals.
ROIs were located within the seizure-generating lobes in the nine subjects. Precision and sensitivity values were 79.18% and 68.88%, respectively, by considering iEEG-detected events as benchmarks. A higher number of ripples were detected inside the ROI compared to the same region in the contralateral lobe.
The evaluation of interictal ripples using non-invasive techniques can help in the delimitation of the epileptogenic zone and guide placement of intracranial electrodes. This is the first study that automatically detects ripples in MEG in the time domain located within the clinically expected epileptic area taking into account the time-frequency characteristics of the events through the whole signal spectrum. The algorithm was tested against intracranial recordings, the current gold standard. Further studies should explore this approach to enable the localization of noninvasively recorded HFOs to help during pre-surgical planning and to reduce the need for invasive diagnostics.
在癫痫中，高频振荡（HFOs）与致痫区（SOZ）密切相关。从头皮脑电图（EEG）和脑磁图（MEG）的非侵入性信号中检测 HFOs 仍然是一项具有挑战性的任务。本研究的目的是通过基于波束形成器的虚拟传感器（VS）减少高频噪声，应用自动程序探索检测到的事件的时频内容，从而自动检测 MEG 信号中的锐波。
从 9 名耐药性癫痫患者中选择 200 秒的 MEG 信号和同步 iEEG。实施了两阶段算法。首先，对头进行波束形成，在 MEG-VS 的粗网格内划定感兴趣区域（ROI）。其次，在 ROI 中计算了一个使用更细网格的波束形成器。使用 Stockwell 变换提供的时频响应自动检测锐波。通过与同步 iEEG 信号的比较来评估性能。
在 9 名受试者中，ROI 位于产生癫痫的脑叶内。考虑到 iEEG 检测到的事件作为基准，精度和灵敏度值分别为 79.18%和 68.88%。与对侧叶相同区域相比，ROI 内检测到的锐波数量更多。
使用非侵入性技术评估间发性锐波有助于划定致痫区，并指导颅内电极的放置。这是第一项在考虑事件的时频特征的情况下，通过整个信号频谱，在临床上预期的癫痫区域内自动检测 MEG 时域中的锐波的研究。该算法经过颅内记录测试，这是当前的金标准。进一步的研究应该探索这种方法，以实现非侵入性记录的 HFOs 的定位，以帮助术前规划，并减少对侵入性诊断的需求。
