SVM-Based System for Prediction of Epileptic Seizures From iEEG Signal.
SVM-Based System for Prediction of Epileptic Seizures From iEEG Signal.
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DOI：
10.1109/tbme.2016.2586475
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发表时间：
2017-05
期刊：
影响因子：
--
通讯作者：
Worrell GA
中科院分区：
文献类型：
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作者：
Shiao HT;Cherkassky V;Lee J;Veber B;Patterson EE;Brinkmann BH;Worrell GA
本文描述了一种通过颅内脑电图（iEEG）记录大脑活动来预测癫痫发作的数据分析建模方法。尽管人们普遍认为癫痫发作前脑电图信号的统计特征会发生变化，但由于数据分析模型的主题特殊性，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临床考虑因素对基于脑电图的癫痫发作预测的重要性，并将这些临床考虑因素适当地转化为数据分析建模假设。在前处理和后处理过程中考虑和研究了几种设计选择对癫痫发作预测精度的影响。实验结果表明，本文提出的基于支持向量机的癫痫发作预测系统能够对癫痫犬的前期和间期脑电图进行鲁棒性预测。灵敏度约为90-100%，假阳性率约为0-0.3次/天。结果还表明，良好的预测是针对特定对象的（狗或人），这与早期的研究一致。只有当训练数据包含足够多的癫痫发作，即至少5-7次癫痫发作时，才有可能实现良好的预测性能。该系统使用特定学科建模和非平衡训练数据。该系统还在训练和测试阶段使用了三种不同的时间尺度。
This paper describes a data-analytic modeling approach for 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 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 pre-processing and post-processing are considered and investigated for their effect on seizure prediction accuracy. Our empirical results show that the proposed SVM-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 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.