OBJECTIVE Intracranial EEG covers only a small fraction of brain volume and it is uncertain if a discharge represents a true seizure onset or results from spread. We therefore assessed if there are differences between characteristics of the ictal onset when we are likely to have a true onset, and characteristics of the discharge in regions of spread. METHODS Wavelet based statistical features were extracted in 503 onset and 390 spread channels of 58 seizures from 20 patients. These features were used as predictors in models based on machine learning algorithms such as k-nearest neighbour, logistic regression, multilayer perceptron, support vector machine, random and rotation forest. RESULTS Statistical features (mean, variance, skewness and kurtosis) associated with all wavelet scales were significantly higher in onset than in spread channels. The best classifier, random forest, achieved accuracy of 79.6% and precision of 82%. CONCLUSIONS The signals associated with onset and spread regions exhibit different characteristics. The proposed features are able to classify the signals with good accuracy. SIGNIFICANCE Using our classifier on new seizures could help clinicians gain confidence in having recorded the real seizure onset or on the contrary be concerned that the true onset may have been missed.

中文翻译：

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我们是否会错过癫痫发作：一项基于颅内脑电图的研究

目的 颅内 EEG 仅覆盖大脑体积的一小部分，并且不确定放电是否代表真正的癫痫发作或由扩散引起。因此，我们评估了可能真正发作时的发作特征与扩散区域的放电特征之间是否存在差异。方法 在 20 名患者的 58 次癫痫发作的 503 个发作和 390 个传播通道中提取基于小波的统计特征。这些特征被用作基于机器学习算法的模型中的预测器，例如 k 最近邻、逻辑回归、多层感知器、支持向量机、随机和旋转森林。结果与所有小波尺度相关的统计特征（均值、方差、偏度和峰度）在开始时显着高于在传播通道中。最好的分类器随机森林达到了 79.6% 的准确率和 82% 的准确率。结论与开始和传播区域相关的信号表现出不同的特征。所提出的特征能够以良好的精度对信号进行分类。意义在新的癫痫发作上使用我们的分类器可以帮助临床医生在记录真正的癫痫发作时获得信心，或者相反担心可能错过了真正的发作。