Shiao Han-Tai, Cherkassky Vladimir, Lee Jieun, Veber Brandon, Patterson Edward E, Brinkmann Benjamin H, Worrell Gregory A
IEEE Trans Biomed Eng. 2017 May;64(5):1011-1022. doi: 10.1109/TBME.2016.2586475. Epub 2016 Jun 29.
This paper describes a data-analytic modeling approach for the prediction of epileptic seizures from intracranial electroencephalogram (iEEG) recording of brain activity. Even though it is widely accepted that statistical characteristics of iEEG signal change prior to seizures, robust seizure prediction remains a challenging problem due to subject-specific nature of data-analytic modeling.
Our work emphasizes the understanding of clinical considerations important for iEEG-based seizure prediction, and proper translation of these clinical considerations into data-analytic modeling assumptions. Several design choices during preprocessing and postprocessing are considered and investigated for their effect on seizure prediction accuracy.
Our empirical results show that the proposed support vector machine-based seizure prediction system can achieve robust prediction of preictal and interictal iEEG segments from dogs with epilepsy. The sensitivity is about 90-100%, and the false-positive rate is about 0-0.3 times per day. The results also suggest that good prediction is subject specific (dog or human), in agreement with earlier studies.
Good prediction performance is possible only if the training data contain sufficiently many seizure episodes, i.e., at least 5-7 seizures.
The proposed system uses subject-specific modeling and unbalanced training data. This system also utilizes three different time scales during training and testing stages.
本文描述了一种数据分析建模方法，用于从颅内脑电图（iEEG）记录的脑活动中预测癫痫发作。尽管人们普遍认为癫痫发作前iEEG信号的统计特征会发生变化，但由于数据分析建模具有个体特异性，可靠的癫痫发作预测仍然是一个具有挑战性的问题。
我们的工作强调理解对基于iEEG的癫痫发作预测重要的临床因素，并将这些临床因素正确转化为数据分析建模假设。考虑并研究了预处理和后处理过程中的几种设计选择对癫痫发作预测准确性的影响。
我们的实证结果表明，所提出的基于支持向量机的癫痫发作预测系统能够对患有癫痫的犬类的发作期和发作间期iEEG片段进行可靠预测。灵敏度约为90 - 100%，假阳性率约为每天0 - 0.3次。结果还表明，良好的预测具有个体特异性（犬类或人类），这与早期研究一致。
只有当训练数据包含足够多的癫痫发作事件，即至少5 - 7次发作时，才可能实现良好的预测性能。
所提出的系统使用个体特异性建模和不平衡训练数据。该系统在训练和测试阶段还利用了三种不同的时间尺度。
