Seizure Forecasting and the Preictal State in Canine Epilepsy.
Seizure Forecasting and the Preictal State in Canine Epilepsy.
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DOI：
10.1142/s0129065716500465
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发表时间：
2017-02
影响因子：
8
通讯作者：
Brinkmann BH
中科院分区：
文献类型：
--
作者：
Varatharajah Y;Iyer RK;Berry BM;Worrell GA;Brinkmann BH
预测癫痫发作的能力可以使癫痫患者更好地管理他们的药物和活动，从而可能减少副作用并提高生活质量。预测癫痫发作仍然是一个具有挑战性的问题，但使用颅内脑电图（iEEG）测量的机器学习方法已经显示出希望。开发了一个基于机器学习的管道来处理iEEG记录并生成癫痫发作警告。结果支持以大于泊松随机预测器的速率预测癫痫发作的能力，用于测试的所有特征集和机器学习算法。此外，受试者特定的神经生理学变化的多个功能报告前铅癫痫发作，提供证据支持存在一个独特的和可识别的发作前状态。
The ability to predict seizures may enable patients with epilepsy to better manage their medications and activities, potentially reducing side effects and improving quality of life. Forecasting epileptic seizures remains a challenging problem, but machine learning methods using intracranial electroencephalographic (iEEG) measures have shown promise. A machine-learning-based pipeline was developed to process iEEG recordings and generate seizure warnings. Results support the ability to forecast seizures at rates greater than a Poisson random predictor for all feature sets and machine learning algorithms tested. In addition, subject-specific neurophysiological changes in multiple features are reported preceding lead seizures, providing evidence supporting the existence of a distinct and identifiable preictal state.