Prediction of seizure outcome improved by fast ripples detected in low-noise intraoperative corticogram
Prediction of seizure outcome improved by fast ripples detected in low-noise intraoperative corticogram
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DOI：
10.1016/j.clinph.2017.03.038
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发表时间：
2017-07-01
影响因子：
4.7
通讯作者：
Sarnthein, Johannes
中科院分区：
文献类型：
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作者：
Fedele, Tommaso;Ramantani, Georgia;Sarnthein, Johannes
目的：术中皮质描记图中的快波（FR，250-500 Hz）最近被提出作为癫痫患者手术结果的特异性预测因子。然而，在线FR检测受到其低信噪比的限制。在这里，我们提出了低噪声EEG与无监督FR detection.Methods的集成：切除术前和切除术后ECoG（N = 9例）同时记录由商业设备（CD）和定制的低噪声放大器（LNA）。FR由先前在不同dataset.Results中的视觉标记上验证的自动检测器进行分析：在所有记录中，在FR频带中，LNA中的背景噪声低于CD中的背景噪声（p < 0.001）。LNA记录的FR率高于CD记录（0.9 +/- 1.4 vs 0.4 +/- 0.9，p < 0.001）。比较切除后ECoG和手术结果的FR率，CD和LNA的阳性预测值PPV = 100%，CD的阴性预测值NPV = 38%，LNA的阴性预测值NPV = 50%。CD和LNA的预测准确率分别为44%和67%.Conclusions：癫痫发作结果的预测得到改善的低噪声EEG和无监督FR detection.Significance的最佳整合：准确，自动化和快速FR评级是必不可少的考虑FR在术中设置。(C)2017由Elsevier爱尔兰有限公司代表国际临床神经生理学联合会发布。
Objective: Fast ripples (FR, 250-500 Hz) in the intraoperative corticogram have recently been proposed as specific predictors of surgical outcome in epilepsy patients. However, online FR detection is restricted by their low signal-to-noise ratio. Here we propose the integration of low-noise EEG with unsupervised FR detection.Methods: Pre-and post-resection ECoG (N = 9 patients) was simultaneously recorded by a commercial device (CD) and by a custom-made low-noise amplifier (LNA). FR were analyzed by an automated detector previously validated on visual markings in a different dataset.Results: Across all recordings, in the FR band the background noise was lower in LNA than in CD (p < 0.001). FR rates were higher in LNA than CD recordings (0.9 +/- 1.4 vs 0.4 +/- 0.9, p < 0.001). Comparison between FR rates in post-resection ECoG and surgery outcome resulted in positive predictive value PPV = 100% in CD and LNA, and negative predictive value NPV = 38% in CD and NPV = 50% for LNA. Prediction accuracy was 44% for CD and 67% for LNA.Conclusions: Prediction of seizure outcome was improved by the optimal integration of low-noise EEG and unsupervised FR detection.Significance: Accurate, automated and fast FR rating is essential for consideration of FR in the intraoperative setting. (C) 2017 Published by Elsevier Ireland Ltd on behalf of International Federation of Clinical Neurophy-siology.