To identify precursors of epileptic seizures, an EEG characteristic analysis is carried out based on a roughness-length method, where fractal dimensions and intercept values are extracted to measure the structure complexity and the amplitude roughness of EEG signals in different phases. Using the significant changes of the fractal dimension and intercept in the preictal phase with respect to those in the interictal phase, a patient-specific seizure prediction algorithm is then proposed by combining with a gradient boosting classifier. The probabilistic outputs of the trained gradient boosting classifier are further processed by threshold comparison and rule-based judgment to distinguish preictal EEG from interictal EEG and to generate seizure alerts. The prediction algorithm was evaluated on 20 patients’ intracranial EEG recordings from the Freiburg EEG database, which contains the preictal periods of 65 seizures and 499[Formula: see text]h interictal EEG. Setting the seizure prediction horizon as 2[Formula: see text]min, averaged sensitivity values of 90.42% and 91.67% with averaged false prediction rates of 0.12/h and 0.10/h were achieved for seizure occurrence periods of 30 and 50[Formula: see text]min, respectively. These results demonstrate the ability of fractal dimension and intercept metrics in predicting the occurrence of seizures.

中文翻译：

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基于粗糙度长度的颅内脑电图特征分析和癫痫发作预测

为了识别癫痫发作的前兆，基于粗糙度长度方法进行脑电特征分析，其中提取分形维数和截距值以测量不同阶段脑电信号的结构复杂性和幅度粗糙度。利用发作前阶段分形维数和截距相对于发作间期的显着变化，结合梯度增强分类器提出了一种针对患者的癫痫发作预测算法。训练后的梯度增强分类器的概率输出通过阈值比较和基于规则的判断进一步处理，以区分发作前脑电图和发作间期脑电图并生成癫痫警报。预测算法对来自弗莱堡脑电图数据库的 20 名患者的颅内脑电图记录进行了评估，其中包含 65 次癫痫发作的发作前期和 499[公式：见文本]h 发作间期脑电图。将癫痫发作预测范围设置为 2[公式：见文本]min，对于癫痫发作发生周期为 30 和 50 [公式：见文本]分钟，分别。这些结果证明了分形维数和截距指标在预测癫痫发作发生方面的能力。对于 30 和 50 [公式：见正文] 分钟的癫痫发作期，分别实现了 67% 的平均错误预测率为 0.12/h 和 0.10/h。这些结果证明了分形维数和截距指标在预测癫痫发作发生方面的能力。对于 30 和 50 [公式：见正文] 分钟的癫痫发作期，分别实现了 67% 的平均错误预测率为 0.12/h 和 0.10/h。这些结果证明了分形维数和截距指标在预测癫痫发作发生方面的能力。